The Data Blog

How to write a research grant proposal?

Posted on 2021-03-09 by Philippe Fournier-Viger

Today, I will discuss how to write a good research grant proposal. This topic is important for researchers, who are at the beginning of their careers and want to obtain funding for their research projects. A good research proposal can be career-changing as it may allow to secure considerable funding that may for example, help to obtain a promotion. On the other hand, a poorly prepared research proposal is likely to fail. To avoid writing a very long post on this topic, I will focus on the key points for writing a good project proposal.

Preparation

Before writing a research grant proposal, the first step is preparation. Preparation should ideally start several weeks or months before the deadline. The reason is that writing a proposal takes time and that unexpected events may occur, which may delay the progress. Moreover, starting earlier allows to ask feedback from peers and to think more about the content and how to improve the proposal.

Another important aspect of preparation is to choose an appropriate funding program for the proposed research project.

The research question

A key aspect of preparing a research grant proposal is to choose a research question that will be addressed by the research project.

The key points to pay attention related to the research question are that: (1) the research question is new and relevant, (2) the research project is feasible within the time frame, using the proposed methodology and given the researcher(s)’s background and skills, and (3) the research project is expected to have an important impact (be useful). In the project proposal, the above elements (1), (2), and (3) need to be clearly explained to convince the reviewers that this project deserved to be funded.

Choosing a good research question takes time, but it is very important.

Literature review

Another important part of a project proposal is the literature review, which should provide an overview of relevant and recent studies on the same topic. It is important that the literature review is critical (highlight the limitations of previous studies) with respect to the research question. Moreover, the literature review can be used to highlight the importance of the research question, and its potential impact and applications.

References should be up-to-date (preferably from the last five years). But older references can be included, especially if they are the most relevant.

Methodology

A good proposal should also clearly explain the methodology that will be used, and the theoretical basis for using that methodology.

About carrying out experiments, one should explain how participants will be recruited and/or data will be obtained, how big the sample size will be(to ensure that results are significant), how results will be interpreted, and how do deal with issues related to ethics.

If a methodology is well-explained and detailed, it indicates that the researcher has a clear plan about how he will conduct the research. This is important to show that the project is feasible.

Timeline of the project

To further convince reviewers that the project will succeed, it is important to also provide a clear timeline for the project. That timeline should indicate when each main task will be done, by who, and what will be the result or deliverables for each task. For example, one could say that during the first 6 months, a PhD student will do a literature review and write a journal paper, while another student will collect data, and so on.

The timeline can be represented visually. For example, I show below a timeline that I have used to apply for some national research funding. That project was a five year projects with three main tasks. I have divided the task clearly among several students.

Note that it is good to mention the names of the students or staff involved in the project, if the names are known. It can also be good to explain how the students will be recruited.

Equipment

It is also useful to mention the equipment or facilities that are available at the institution where the researcher works, and that will help to carry the project.

Impact

Another very important aspect is to write clearly what will be the expected impact of the research project. The impact can be described in terms of advances in terms of knowledge, but also in terms of benefits to the society or economy. In other words, the proposal should explain why the project will be useful.

Budget

A project proposal needs to also include a budget that must follow the guidelines of the targeted funding source. It is important that the amounts of money are reasonable and justifications are provided to explain why the money is required.

Team, institution, and Individual

A proposal should also explain that the applicant and its team have the suitable background and skills required for successfully conducting the project. This is done by describing the background and skills of researchers, and how they fit the project.

Conclusion

In this blog post, I have discussed the key points that a good research proposal should include. I could say more about this topic, but I wanted to not make it too long for a blog post.

—
Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Research | Tagged academia, fund, funding, grant, grant proposal, professor, Research, research fund, researcher, university | Leave a comment

Some funny or interesting websites related to research

Posted on 2021-03-08 by Philippe Fournier-Viger

Today I will talk about a few websites that are somewhat funny and are related to research.

Auto-reject ( https://autoreject.org/ )

This is a website that let you automatically generate reviews to reject papers. The reviewer need to check some boxes about what is the content of the paper, and then a review is generated. Note that it is not meant to be used by reviewers in real-life. It was created as an April fool’s joke. But it is interesting to try and see what kind of review is generated. For example, I generated a review:

Dear Author,
Thank you very much for your submission. We have enclosed detailed reviews of your paper below.

Reviewer 2

Generally speaking, this is interesting work which may well find some readers in the community. However, in its present form, the paper suffers from a number of issues, which are outlined below.

Area

Computer Science is not new. The past decade has seen a flurry of papers in the area, which would indicate progress and interest. However, the impact outside of Computer Science has been very limited, as is easily reflected in the ever-decreasing number of citations. Unfortunately, Computer Science finds itself in a sandwich position between a more theoretical community and a more practical community, which can both claim a bigger intellectual challenge and a greater relevance in practical settings. This is especially true for the field of Data mining, where the last big progress is now decades ago. As refreshing it feels to finally again see progress claimed in the field, the present paper must be read and evaluated in this context.

Approach
The paper suffers from an excess of formalism. Several important definitions are introduced formally only, without a chance to clearly understand their motivation or usage. The authors should be encouraged to make more use of informal definitions, building on the well-established formal definitions in the community.

For readers, it is helpful to see the approach being demonstrated in a limited setting that facilitates control and comprehension. However, real-world settings are much more detailed, and much more challenging. If the approach is ever to succeed in practice, having a demonstrator that copes with real-world conditions is a must; the authors would be encouraged to work with industry to get access to appropriate settings and data sets.

Evaluation
This reviewer read until Page 2, and the evaluation seems to be okay.
The fact that your evaluation involves students is also a cause for concerns - not only for ethical reasons, but also for the sake of generality. Generally speaking, results achieved with students cannot generalize to results achieved with the general public - especially not in Computer Science. You have to run your evaluation and experiments involving a wider range of subjects.
Limitations
The section on 'future work' fits into this very picture: There clearly is still a lot of additional work to do until this approach can begin to sho (...)

The code of that website is open-source but the approach is really simple if you look under the hood. A review is generated using some simple IF conditions.

Sh**t my reviewer say ( https://shitmyreviewerssay.tumblr.com/ )

If you have submitted several papers to journals and conferences, you will know that sometimes, we are not lucky and some reviewers can give some very bad comments rather than adopting a constructive approach. This website lists several bad comments given by reviewers. Here are a few examples:

“Indeed, by the end of the paper, the reader is left with a feeling of ‘so what now?’”
” The fact that the question of this paper has never been asked should, on balance, count against the paper.”
“Is this really a discovery or just the confirmation of math?”
“I don’t see how your approach has potential to shed light on a question that anyone might have.”
“The paper could be considered for acceptance given a rewrite of the paper and change in the title and abstract. “
” This discussion might be better directed to a different audience, perhaps an undergraduate class “
“I am personally offended that the authors believed that this study had a reasonable chance of being accepted to a serious scientific journal.”

PhD Comics (http://phdcomics.com/ )

If you don’t know this website already, it offers some good cartoons about graduate student life. There are hundreds of comic strips, and some are really funny and to the point. When, I was a graduate student, I keep reading this every week. Although the author seems to have stopped uploading new comic strips, there is a big archive of old comics. There are also two movies that were made based on these comics called the PHD Movie 1 and PHD Movie 2.
The 200 most popular comic strips from this website:
http://phdcomics.com/comics/most_popular.php

An example of comic from PhDComics – Copyright (c) Jorge Cham

SCIGEN – Automatic research paper generator ( https://pdos.csail.mit.edu/archive/scigen/ )

This website offers a tool to randomly generate computer science research papers. This was designed as a joke. But it is funny to try it and see what is generated. It can even generate some graphs and charts. To use the tool, all you need to do is fill the author names and click “Generate“:

Although papers generated by this tool generally do not make sense when you look at the details, some people have submitted generated papers to some predatory conferences to show that there is no review process and that the goal is just to get some money. You can see more details about that story on the website of SCIGEN.

Conclusion

That is all for today. I just wanted to share a few interesting websites that are not useful for your research but I think are funny or interesting.

If you have some other good websites, you may post them in the comment section below.

—
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 120 data mining algorithms.

Posted in Academia, General | Tagged academia, Research, website | Leave a comment

How to cite equations in a research paper?

Posted on 2021-03-05 by Philippe Fournier-Viger

Today, I will talk about how to cite equations in research papers, and give a few advices that may be useful to researchers. The idea of this blog post comes from a discussion that I had with some colleagues about the proper way of citing equations. As you know, some papers will contains equations like this:

   u= q × p     (1)

And then authors will also refer to this equation in the text such as “Equation (1) explains how to calculate the utility of a pattern“. This way of citing equations is generally viewed as acceptable and very common. But is this the best way of refering to equations?

There are a few things that need to be discussed.

First, there are researchers that will say that “equation” is a word with a broad meaning and that it is better to use a more specific term when possible. Following this principe, one could say “Formula (1) explains how to calculate the utility of a pattern” in the above example. There are several terms that can be used depending on the context such as “formula“, “reccurrence“, “inequality” and “identify“. So what is the difference?

A mathematical expression is an mathematical phrase that contains some numbers or variables connected by some operators. For example, 1+1 is an expression, and 1+1=2 is also an expression.
An equation is a mathematical expression that contains the = symbol. For example 1 = 1 is an equation, and a = b + c is also an equation. Such statements can also be called an equality.
An identify is an equality that is true no matter what values are given to the variables that it contains. For example, x + x = x × 2 is an identify.
An inequality is a matematical expression where we compare two expression that are not equal by using the symbols such as < > = ≥ ≤ ≠. For example, x+c < d is an inequality. It is not an equation!
The definition of a formula is quite broad and some will disagree on the exact meaning. But generally, a formula expresses how to calculate some variable based on one or more variables. For example, equation (1) in the example is a formula for calculating the utility of a pattern. It tells how to find the value of a variable “u” from the values of two variables “q” and “p”. Thus, it can be called a formula. Another example is the Pythagorean theorem which is a formula that can be written as an equation: a^2 + b^2 = c^2.
and there are others…

Second, some researchers will suggest to avoid using abbreviations to refer to equations such as “eq. (1)” and “eqn. (1) but instead to write in full “equation (1)” or “formula (1)”.

Third, it is recommended to not just refer to equations in the text but also to add some words to help the reader to remember what the equation is about. This is explained clearly with some examples in the “Handbook of writing for the mathematical sciences“ of Higam:

“When you reference an earlier equation it helps the reader if you add a word or phrase describing the nature of that equation. The aim is to save the reader the trouble of turning back to look at the earlier equation. For example, “From the definition (6.2) of dual norm” is more helpful than “From (6.2)”; and “Combining the recurrence (3.14) with inequality (2.9)“ is more helpful than “Combining (3.14) and (2.9)“. Mermin [200] calls this advice the “Good Samaritan Rule”. As in these examples, the word added should be something more informative than just “equation” (or the ugly abbreviation “Eq.”), and inequalities, implications and lone expressions should not be referred to as equations.” (color and bold formatting added by me)

That is all for today! Hope that this has been interesting and will be useful in your papers.

—
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 120 data mining algorithms.

Posted in Academia, General, Research | Leave a comment

Analyzing the COVID-19 genome with AI and data mining techniques (paper + data + code)

Posted on 2021-02-18 by Philippe Fournier-Viger

Recently, my team has been working on analyzing COVID-19 genome sequences using pattern mining and other data mining and AI techniques. We have recently published a paper in the Applied Intelligence journal about this.

In this blog post, I will give some brief overview of this. The PDF of the paper can be found here:

Nawaz, S., Fournier-Viger, P., Shojaee, A., Fujita, H. (2021). Using Artificial Intelligence Techniques for COVID-19 Genome Analysis. Applied Intelligence, to appear.

And the source code and data: https://github.com/saqibdola/SPM-MA4GSA

The main idea of the paper is the following. We have obtained genome sequences of different strains of the COVID-19 virus. These genome sequences can be viewed as strings of letters (nucleotides). For example, below is four sequences of nucleotides:

Then, after preprocessing these sequences, it is possible to analyze them using pattern mining algorithms and other artificial intelligence techniques. The main process is the following:

First we prepare the data (step 1), and then we apply different techniques (step 2). First, we have applied itemset mining, sequential pattern mining and sequential rule mining techniques to find patterns that are common to many genome sequences. Some examples of sequential patterns (sequences of nucleotides that appear often) find by the CM-SPAM algorithm are below:

This is just to give an overview. Other types of patterns are discussed in the paper in more details.

Second, we also tested sequence prediction models to see if the nucleotides in genome sequences can be predicted. We compared various models offered in the SPMF data mining software and got results like this:

In general, prediction of genome sequence does not give a high accuracy but still better than a random prediction. We discuss these results in more details in the paper.

Third, we also designed some mutation analysis algorithm to compare different strains of the coronavirus. For example, by comparing two strains, we identified some mutations:

That is just a brief overview of the paper!

There are many possibilities for extensions of this research. In particular, various other pattern mining algorithms and machine learning algorithms could be applied as well. Using the code and data provided above, you can also make your own research on this topic! Besides, the tools presented in this paper can also be applied to other genome sequences beside the COVID-19 virus.

Hope this has been interesting.

—
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 120 data mining algorithms.

Posted in artificial intelligence, Big data, Bioinformatics, Data Mining, Data science, Machine Learning, Pattern Mining | Tagged coronavirus, covid, covid19, data, genome, itemset mining, pattern mining, sequence, sequence prediction, sequential pattern | 5 Comments

New version of SPMF (2.44): 4 new algorithms, datasets and features

Posted on 2021-02-12 by Philippe Fournier-Viger

Today, I am happy to announce that a new version of the SPMF open-source data mining software is released (v. 2.44). This is the download page. This new version was made possible due to several contributors.

What is new?

New data mining algorithms:
– LTHUI-Miner for mining the locally-trending high utility itemsets (by Yanjun Yang)
– MLHUI-Miner for discovering the multi-level high utility itemsets (by Ying Wang)
– AER-Miner for mining attribute evolution rules in a dynamic attributed graph (by Ganghuan He)
– TSPIN for mining the top-k stable periodic patterns in a transaction database
(by Ying Wang, Peng Yang)

New datasets:
– Fruithut, Liquor and E-commerce (prepared by Ying Wang)

Some bug fixes:
– Fixed a small bug in DFIN (thanks to Nader Aryabarzan)
– Fixed a bug in the user interface for TKG (thanks to..)
– Fixed a small error in the DB_LHUI.txt example file

Some new tool:
– Added a new tool to fix problems in transactions databases with time and utility information

Some new feature:
– Added a feature for saving or loading a trained sequence prediction model from file (for the AKOM, DG, TDAG, LZ78, PPM, CPT, and CPT+ algorithms)

Wrappers for other languages
– Added a webpage with a list of wrappers for calling some SPMF algorithms from R, Python, Weka and Spark (note: those wrappers were designed by other people and are not an official project of SPMF so be aware that not all algorithms may work… but I think it can be quite useful for some)

What is for the future?

The SPMF project is still under active development. The next version will be released in a few weeks as I still have received several algorithms that are waiting to be added to SPMF. The next release will include several new sequential pattern mining algorithms.

Would you like to contribute to SPMF?

The SPMF project is quite successful due to the numerous contributors that are provided some code, reported bugs and also provided many comments. If you are interested to contribute something (code of some algorithms or other things), please leave me an e-mail at philfv8 AT yahoo DOT com.

Thanks again to all for your support!

Posted in Data Mining, Data science, open-source, Pattern Mining, spmf, Utility Mining | Tagged algorithm, data mining, data science, dataset, open source, open-source, pattern mining, spmf | Leave a comment

How to write an academic book?

Posted on 2021-02-09 by Philippe Fournier-Viger

Have you ever wanted to write an academic book or wondered what are the steps to write one? In this blog post, I will give an overview of the steps to write an academic book, and mention some lessons learned while writing my recent book on high utility pattern mining.

Step 1. Think about a good book idea.
The first step for writing a book is to think about the topic of the book and who will bethe target audience. The topic should be something that will be interesting for an audience. If a book focuses on a topic that is too narrow or target a small audience, the impact may be less than if a more general topic is chosen or if a larger audience is targeted.

One should also think about the content of the book, evaluate how much time it would take to write the book, and think about the benefits of making the book versus spending that time to do something else. It is also important to determine the book type. There are three main types of academic books:

First, one may publish a textbook, reference book or handbook. Such book must be carefully planned and written in a structured way. The aim is to write a book that can be used for teaching or used as a reference by researchers and practitioners. Because such book must be well-organized, all chapters are often written by the same authors.
Second, one may publish an edited book, which is a collection of chapters written by different authors. In that case, the editors typically write one or two chapters and then ask other authors to write the remaining chapters.This is sometimes done by publishing a “call for chapters” online, which invite potential authors to submit a chapter proposal. Then, the editor evaluates the proposal and select some chapters for the book. Writing such book is generally less time-consuming than writing a whole book by oneself because the editors do not need to write all the chapters. However, a drawback of such book is that chapters may contain redundancy and have different writing styles. Thus, the book may be less consistent than a book entirely written by the same authors. A common type of edited book is also the conference or workshop proceedings.
Third, one may publish his Ph.D. thesis as a book if the thesis is well-written. In that case, one should be careful to choose a good publisher because several predatory publishers offer to publish theses with a very low quality control, while taking all the copyrights, and then selling the theses at very expensive prices.

Step 2. Submit a book proposal
After finding a good idea for a book, the next step is to choose a publisher. Ideally, one should choose a famous publisher or a publisher that has a good reputation. This will give credibility to the book, and will help to convince potential authors to write chapters for the book if it is an edited book.

After choosing a publisher, one should write a book proposal and send it to the publisher. Several publishers have specific forms for submitting a book proposal, which can be found on their website or by contacting the publisher. A book proposal will request various information such as: (1) information about the authors or editors, (2) some sample chapter (if some have been written), (3) is there similar books on the market?, (4) who will be the primary and secondary audience?, (5) information about the conference or workshop if it is a proceedings book, (6) how many pages, illustrations and figures the book will contain?, (7) what is the expected completion date?, and (8) a short summary of your book idea and the chapter titles.

The book proposal will be evaluated by the publisher and if it is accepted, the publisher will ask to sign a contract. One should read the contract carefully and then sign it if it is satisfying.

Step 3. Write the book
Then the next step is to write the book, which is generally the most time-consuming part. In the case of a book written all by the same authors, this can require a few months. But for an edited book, it can take much less time. Editor must still find authors for writing the chapters and perhaps also write a few chapters.

After the book have been written, it should be checked carefully for errors and consistency. A good idea is to ask peers to check the book to see if something need to be improved. For an edited book, a review process can be organized by recruiting reviewers to review each chapter. The editors should also spend some time to put all the chapters together and combine them in a book. This can take quite a lot of time, especially if the authors did not respect the required format. For this reason, it is important to give very clear instructions to authors with respect to the format of their chapters before they start writing.

Step 4. Submit the book the publisher
After the book is written, it is submitted to the publisher. The publisher will check the content and the format and may offer other services such as creating a book index or revising the English. A publisher may take a month or two to process a book before publishing it.

Step 5. Promote the book
After writing a book, it is important to promote it in an appropriate on the web, social media, or at academic conferences. This will ensure that the book is successful. Of course, if one choose a good publisher, the book will get more visibility.

Lessons learned
This year, I published an edited book on high utility pattern mining with Springer. I followed all the above steps to edit that book. I first submitted a book proposal to Springer, which was accepted. Then, I signed the contract, and posted a call for chapters. I received several chapter proposals and also asked other researchers to write chapters. The writing part took a bit of time because although I edited the book, I still participated to the writing of six of the twelve chapters. Moreover, I also asked various people to review the chapters. Then, it took me about 2 weeks to put all the chapters together and fix the formatting issues. Overall, the whole process was done over about 1 year and half, but I spent perhaps 1 or 2 months of my time. Would I do it again? Yes, because I think it is a good for my career, and I have some other ideas for books.

The most important lesson that I learned is to give more clear instructions to authors to reduce formatting problems and other issues arising when putting all chapters together.

Conclusion
In this blog post, I have discussed how to write an academic book. Hope you have learned something! Please share your comments below. Thanks for reading!

—
Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Research | Tagged academia, book, publisher, Research, researcher, university, writing | Leave a comment

An introduction to frequent subgraph mining

Posted on 2021-02-05 by Philippe Fournier-Viger

In this blog post, I will give an introduction to an interesting data mining task called frequent subgraph mining, which consists of discovering interesting patterns in graphs (a pattern mining task). This task is important since data is naturally represented as graph in many domains (e.g. social networks, chemical molecules, map of roads in a country). It is thus desirable to analysze graph data to discover interesting, unexpected, and useful patterns, that can be used to understand the data or take decisions.

What is a graph? A bit of theory…

But before discussing the analysis of graphs, I will introduce a few definitions. A graph is a set of vertices and edges, having some labels. Let’s me illustrate this idea with an example. Consider the following graph:

This graph contains four vertices (depicted as yellow circles). These vertices have labels such as “10” and “11”. These labels provide information about the vertices. For example, imagine that this graph is a chemical molecule. The label 10 and 11 could represent the chemical elements of Hydrogen and Oxygen, respectively. Labels do not need to be unique. In other words, the same labels may be used to describe several vertices in the same graph. For example, if the above graph represents a chemical molecule, the labels “10” and “11” could be used for all vertices representing Oxygen and Hydrogen, respectively.

Now, besides vertices, a graph also contains edges. The edges are the lines between the vertices, here represented by thick black lines. Edges also have some labels. In this example, four labels are used, which are 20, 21, 22 and 23. These labels represents different types of relationships between vertices. Edge labels do not need to be unique.

Types of graphs: connected and disconnected

Many different types of graphs can be found in real-life. Graphs are either connected or disconnected. Let me explain this with an example. Consider the two following graphs:

The graph on the left is said to be a connected graph because by following the edges, it is possible to go from any vertex to any other vertices. For example, imagine that vertices represents cities and that the edges are roads between cities. A connected graph in this context is a graph where it is possible to go from any city to any other cities by following the roads. If a graph is not connected, it is said to be a disconnected graph. For example, the graph on the right is disconnected since Vertex A cannot be reached from the other vertices by following the edges. In the following, we will use the term “graph” to refer to connected graphs. Thus, all the graphs that we will discuss in the following paragraphs will be connected graphs.

Types of graphs: directed and undirected

It is also useful to distinguish between directed and undirected graphs. In an undirected graph, edges are bidirectional, while in a directed graph, the edges can be unidirectional or bidirectional. Let’s illustrate this idea with an example.

The graph on the left is undirected, while the graph on the right is directed. What are some real-life examples of a directed graph? For example, consider a graph where vertices are locations and edges are roads. Some roads can be travelled in both directions while some roads may be travelled in only a single direction (“one-way” roads in a city).

Some data mining algorithms are designed to work only with undirected graphs, directed graphs, or support both.

Analyzing graphs

Now that we have introduced a bit of theory about graphs, what kind of data mining task can we do to analyze graphs? There are many answers to this question. The answer depends on what is our goal but also on the type of graph that we are analyzing (directed/undirected, connected/disconnected, a single graph or many graphs).

In this blog post, I will explain a popular task called frequent subgraph mining. The goal of subgraph mining is to discover interesting subgraph(s) appearing in a set of graphs (a graph database). But how can we judge if a subgraph is interesting? This depends on the application. The interestingness can be defined in various ways. Traditionally, a subgraph has been considered as interesting if it appears multiple times in a set of graphs. In other words, we want to discover subgraphs that are common to multiple graphs. This can be useful for example to find association between chemical elements common to several chemical molecules.

The task of finding frequent subgraphs in a set of graphs is called frequent subgraph mining. As input the user must provide:

a graph database (a set of graphs)
a parameter called the minimum support threshold (minsup).

Then, a frequent subgraph mining algorithm will enumerate as output all frequent subgraphs. A frequent subgraph is a subgraph that appears in at least minsup graphs from a graph database. For example, let’s consider the following graph database containing three graphs:

Now, let’s say that we want to discover all subgraphs that appear in at least three graphs. Thus, we will set the minsup parameter to 3. By applying a frequent subgraph mining algorithm, we will obtain the set of all subgraphs appearing in at least three graphs:

Consider the third subgraph (“Frequent subgraph 3”). This subgraph is frequent and is said to have a support (a frequency) of 3 since it appears in three of the input graphs. These occurrences are highlighted in red, below:

Now a good question is how to set the minsup parameter? In practice, the minsup parameter is generally set by trial and error. If this parameter is set too high, few subgraphs will be found, while if it is set too low, hundred or millions of subgraphs may be found, depending on the input database.

Now, in practice, which tools or algorithms can be used to find frequent subgraphs? There exists various frequent subgraph mining algorithms. Some of the most famous are GASTON, FSG, and GSPAN.

Mining frequent subgraphs in a single graph

Besides discovering graphs common to several graphs, there is also a variation of the problem of frequent subgraph mining that consists of finding all frequent subgraphs in a single graph rather than in a graph database. The idea is almost the same. The goal is also to discover subgraphs that appear frequently or that are interesting. The only difference is how the support (frequency) is calculated. For this variation, the support of a subgraph is the number of times that it appears in the single input graph. For example, consider the following input graph:

This graph contains seven vertices and six edges. If we perform frequent subgraph mining on this single graph by setting the minsup parameter to 2, we can discover the five following frequent subgraphs:

These subgraphs are said to be frequent because they appear at least twice in the input graph. For example, consider “Frequent subgraph 5”. This subgraph has a support of 2 because it has two occurrences in the input graph. Those two occurrences are highlighted below in red and blue, respectively.

Algorithms to discover patterns in a graph database can often be adapted to discover patterns in a single graph.

Want to try frequent subgraph mining?

If you want to try frequent subgraph mining algorithms, some public fast Java open-source implementations of TKG for top-k frequent subgraph mining and gSpan are available in the SPMF data mining library. The SPMF library also offers algorithms for many other pattern mining tasks such as high utility itemset mining , sequential pattern mining, sequential rule mining and periodic pattern mining.

Conclusion

In this blog post, I have introduced the problem of frequent subgraph mining, which consists of discovering subgraphs appearing frequently in a set of graphs. This data mining problem has been studied for more than 15 years, and many algorithms have been proposed. Some algorithms are exact algorithms (will find the correct answer), while some other are approximate algorithms (do not guarantee to find the correct answer, but may be faster).

Some algorithms are also designed to handle directed or undirected graphs, or mine subgraphs in a single graph or in a graph database, or can do both. Besides, there exists several other variations of the subgraph mining problem such as discovering frequent paths in a graph, or frequent trees in a graph.

Besides, in data mining in general, many other problems are studied related to graphs such as optimization problems, detecting communities in social networks, relational classification, etc.

In general, problems related to graphs are quite complex compared to some other types of data. One of the reason why subgraph mining is difficult is that algorithms typically need to check for “subgraph isomorphisms“, that is to compare subgraphs to determine if they are equivalent. But nonetheless, I think that these problems are quite interesting as there are several research challenges.

I hope that you have enjoyed this blog post. If there is some interest about this topic, I may do another blog post on graph mining in the future.

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Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 120 data mining algorithms.

Posted in Big data, Data Mining, Pattern Mining | Tagged data mining, graph, gspan, pattern mining, subgraph, subgraph mining, tkg | 12 Comments

Sequential pattern mining vs Sequence prediction ?

Posted on 2021-02-04 by Philippe Fournier-Viger

In this blog post, I will answer a question that I have received in my e-mail about what is the difference between sequential pattern mining and sequence prediction. I think that this is a good question and sharing the answer can help to clarify some concepts for some people.

Generally speaking, the goal of sequential pattern mining is to find some patterns that appear in many sequences of symbols. For example, lets say that you have some sequences of purchases made by customers in a retail store. You can then apply a sequential pattern mining algorithm to find sequential patterns, that is to know what are some sequence of purchases that are common to many customers. For example, you may find that <harrypotter1, spiderman, batman> is a sequential pattern. This pattern means that many people have bought the movie Harry potter 1, and then Spiderman, and then Batman. If you find such patterns, it can help you to understand the data. If you are the retail store manager, you may use such pattern to take some business decisions such as to offer some discount to customers on Batman if they previously buy harrypotter and spiderman.

But there are many other usages of sequential patterns. You can also use the sequential patterns to make some sequence prediction. For example, if someone buys Harry Potter 1 and Spiderman, you may predict that he will buy Batman based on the above sequential pattern. This can be used to perform recommendation

Another example about the applications of sequential pattern mining is to find patterns in text documents. A text document is a set of sentences, and each sentences is a sequence of words. Thus, you can apply a sequential pattern mining algorithm to find the sequential patterns that tell you some frequent sequence of words appearing many times in a book. This can tell you about some writing patterns used by some authors, and you can even use these patterns to try to guess who is the author of some anonymous book (if you are curious, I actually did that in a paper: https://www.philippe-fournier-viger.com/FLAIRS2016__AUTHORSHIP_ATTRIBUTION.pdf).

On the other hand, the goal of sequence prediction is to predict what is the next symbol of a sequence of symbols. For example, some people buy the movies Harry Potter 1, Hulk, Batman, and then Star Wars, and we want to know what is the next movie that this person will buy? There are many ways to do sequence prediction. One way is to use the sequential patterns or a variation called sequential rules. For example, we did sequence prediction using sequential rules in apaper to predict the next webpage that someone will click: https://www.philippe-fournier-viger.com/sequential_rules_prediction_2012.pdf
But there are also many other models for sequence predictions that do not rely on sequential patterns like the CPT and CPT+ models (video presentation here: https://data-mining.philippe-fournier-viger.com/video-sequence-prediction-with-the-cpt-and-cpt-models/) , the all-k order markov model, the DG model, TDAG, and LZ78.

Thus, to summarize, the goal of sequential pattern mining is to find patterns. You can find these patterns in data for multiple purpose. It can be just to understand the data and learn something about it. It can be to use these patterns to do sequence prediction, or other tasks like clustering, authorship attribution, etc. Thus, sequential pattern mining has many applications and sequence prediction is one of them. And the goal of sequence prediction is to predict the next symbol of a sequence. There are many methods to do sequence prediction and sequential pattern mining is one of them.

Hope that his short answer will be helpful. Some additional blog posts that I wrote on these topics:

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Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 180 data mining algorithms.

Posted in Big data, Data Mining | Tagged data science, pattern mining, sequence, sequence prediction, sequential pattern | 2 Comments

Email invitation to be a “special” speaker, a scam?

Posted on 2021-02-01 by Philippe Fournier-Viger

Have you ever received an e-mail from some small conference telling you that they want to invite you to give a talk as a special speaker, honorary speaker or distinguished speaker? I received several e-mails like this and while something it is some valuable invitation, most of the time is just spam. In this blog post, I will discuss this.

Several small conference organizers send unsollicited e-mails to try to attract papers for their conferences and ultimately to collect money in the form of registration fees. As many people ignore these e-mails, some strategies that they use is to mention the title of one of your recent paper and to invite you as a special speaker. This is an example:

Dear Dr. XXXXXX
Please accept my apology if this email bothers you, as I have tried to send you this invitation in last months but without any response from you.
On behalf of the Organizing Committee, it is our delight to extend to NAME_OF_CONFERENCEwhich is going to be held during 2021 (next year) in CITY, Japan.It is our great pleasure and privilege to welcome you to join the NAME_OF_CONFERENCE act as the chair/speaker while presenting about TITLE OF_MY_PAPER.

Another example:

Dear Dr. XXXXX,
Hope you receive this letter in a wonderful mood. Please accept my apology if this email bothers you, as I have tried to send you this invitation to you but without any response. Would you please send a reply?
Thanks for your time to this email from our committee, the committee of XXXXXX-2022 cordially welcome you to share a presentation as a session speaker/chair regarding your research. Sincerely wish you can give us an opportunity to include your research in our program and proceeding.

If you read such e-mails for the first time, you may think that the sender has read your paper and is really interested in your research and wants to invite you as a special guest to their conference to give a talk. So you may be tempted to accept. However, most of the time, this is just SPAM and they only mention the title of one of your paper and that you are invited as a special speaker to catch your attention.

Before accepting such invitation, you should check: (1) is this a well-known conference in your field that has been held for several years? (2) do you know other people having attended it? (3) is it associated with a famous institutions or publisher (beware that the website may be fake and provide misleading information, though). But perhaps the most important is to check if the conference organizers will be asking you to pay a registration fee or give you some special benefits. The reason is that as an invited guest, you would expect some kind of preferential treatment over regular attendees. If they do not give you any special benefits as a special speaker, then you are just another speaker for their conference, and the goal is just to earn money. If you are not sure about whether a conference is legit or not, it is best also to ask your supervisor or senior researchers for their opinion.

There are also many other types of SPAM e-mails that target academic researchers such as e-mails asking you to publish your thesis as a book. I may talk about this in more details in a future blog post! That is all for today!

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Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Research | Tagged academia, email, spam, speaker | Leave a comment

Two journal special issues with deadlines in 2021

Posted on 2021-01-28 by Philippe Fournier-Viger

This is a short blog post tto let you know that I am co-organizing two special issues in some journals related to data mining, data science and machine learning:

(1) Special issue on Spatiotemporal Big Data Analytics
Journal: Electronics
Publisher: MDPI
Deadline: 30th September 2021
Details: https://www.mdpi.com/journal/electronics/special_issues/Spatiotemporal-BD-Analytics

(2) Special issue on Generative Adversarial Networks for Multi-Modal Multimedia Computing
Journal: Wireless Communication and Mobile Computing
Publisher: Wiley / Hindawi
Deadline: 28th May 2021
Link: https://www.hindawi.com/journals/wcmc/si/710287/

Note that those are open-access journal. Thus, there is a publication fee. If you do not want to have a publication fee, you may consider submitting your paper to the Data Science and Pattern Recognition journal (DSPR), which is currently free to publish and has a fast review time. I am editor-in-chief of that journal.

If you have any questions or are not sure whether your research is relevant to these journals, you may send me a message, and we can discuss about it.

—
Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Posted in cfp | Leave a comment

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Two journal special issues with deadlines in 2021

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