What Is Frequent in a Single Graph?

Bringmann, Björn; Nijssen, Siegfried

doi:10.1007/978-3-540-68125-0_84

Cited by 161 publications

(160 citation statements)

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“…These problems exploit a transactional view of the data, i.e., they are represented under the form of a collection of transactions and frequent patterns are those mapped to at least a given number of transactions. Among pattern mining problems that do not enter this family, an example is the relatively recent problem of mining patterns in one unique large graph or network, addressed for instance in [3].…”

Section: Definition 1 (Transactional Frequent Pattern Mining)mentioning

confidence: 99%

Mining Representative Frequent Patterns in a Hierarchy of Contexts

Rabatel

Bringay

Poncelet

2014

Advances in Intelligent Data Analysis XIII

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Abstract. More and more data come with contextual information describing the circumstances of their acquisition. While the frequent pattern mining literature o↵ers a lot of approaches to handle and extract interesting patterns in data, little e↵ort has been dedicated to relevantly handling such contextual information during the mining process. In this paper we propose a generic formulation of the contextual frequent pattern mining problem and provide the CFPM algorithm to mine frequent patterns that are representative of a context. This approach is generic w.r.t. the pattern language (e.g., itemsets, sequential patterns, subgraphs, etc.) and therefore is applicable in a wide variety of use cases. The CFPM method is experimented on real datasets with three di↵erent pattern languages to assess its performances and genericity.

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Section: Definition 1 (Transactional Frequent Pattern Mining)mentioning

confidence: 99%

Mining Representative Frequent Patterns in a Hierarchy of Contexts

Rabatel

Bringay

Poncelet

2014

Advances in Intelligent Data Analysis XIII

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“…This problem has received some attention in the network mining literature (Bringmann & Nijssen, 2008;Kuramochi & Karypis, 2004;Fiedler & Borgelt, 2007) and there are several alternative formulations for anti-monotonic notions of frequency. It is natural to use the notion of relative frequency defined as…”

Section: Frequent Setsmentioning

confidence: 99%

“…For the Cora dataset, frequent patterns correspond to trends in the data such as "authors of neural networks papers are also authors of image processing papers". This type of network mining has been studied before, with different applications, for example, in web-graph mining (Bringmann & Nijssen, 2008;Kuramochi & Karypis, 2004;Fiedler & Borgelt, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, for the setting of Claudien (De Raedt & Dehaspe, 1997), using SI and range restriction (with either object identity or θ-subsumption), we provide a full generalization of the LCM algorithm that does not need to store previous sets in the enumeration. The Claudien setting is also closely related to recent efforts to improve efficiency in network mining (Bringmann & Nijssen, 2008). As we show below, one can use the new frequency notion of Bringmann and Nijssen (2008) with LCM, but we cannot use the implicitly assumed underlying semantics, so that closures must still be calculated relative to the SI semantics.…”

Section: Introductionmentioning

confidence: 99%

“…The Claudien setting is also closely related to recent efforts to improve efficiency in network mining (Bringmann & Nijssen, 2008). As we show below, one can use the new frequency notion of Bringmann and Nijssen (2008) with LCM, but we cannot use the implicitly assumed underlying semantics, so that closures must still be calculated relative to the SI semantics.…”

Section: Introductionmentioning

confidence: 99%

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Mining closed patterns in relational, graph and network data

Garriga

Khardon

Raedt

2012

Ann Math Artif Intell

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Recent theoretical insights have led to the introduction of efficient algorithms for mining closed item-sets. This paper investigates potential generalizations of this paradigm to mine closed patterns in relational, graph and network databases. Several semantics and associated definitions for closed patterns in relational data have been introduced in previous work, but the differences among these and the implications of the choice of semantics was not clear. The paper investigates these implications in the context of generalizing the LCM algorithm, an algorithm for enumerating closed item-sets. LCM is attractive since its run time is linear in the number of closed patterns and since it does not need to store the patterns output in order to avoid duplicates, further reducing memory signature and run time. Our investigation shows that the choice of semantics has a dramatic effect on the properties of closed patterns and as a result, in some settings a generalization of the LCM algorithm is not possible. On the other hand, we provide a full generalization of LCM for the semantic setting that has been previously used by the Claudien system.

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A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

Moussaoui

Zaghdoud

Akaichi

2018

Concurrency and Computation

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Summary Since the appearance of social networks, there was a historic increase of data. Unfortunately, terrorists are taking advantage of the easiness of accessing social networks and they have set up profiles to recruit, radicalize, and raise funds. Most of these profiles have pages that exist as well as new recruits to join the terrorist groups, see, and share information. Therefore, there is a potential need for detecting terrorist communities in social networks in order to search for key hints in posts that appear to promote the militants' cause. In order to remedy this problem, we first use a possibilistic‐clustering algorithm that allows more flexibility when assigning a social network profile to clusters (non‐terrorist, terrorist‐sympathizer, terrorist). Then, we introduce a new possibilistic flexible graph mining method to discover similar subgraphs by applying possibilistic similarity rather than using hard structural exact similarity. We experimentally show the efficiency of our possibilistic approach through a detailed process of tweets extract, semantic processing, and classification of the community detection.

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What Is Frequent in a Single Graph?

Cited by 161 publications

References 5 publications

Mining Representative Frequent Patterns in a Hierarchy of Contexts

Mining Representative Frequent Patterns in a Hierarchy of Contexts

Mining closed patterns in relational, graph and network data

A possibilistic framework for the detection of terrorism‐related Twitter communities in social media

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