A survey of uncertainty handling in frequent subgraph mining algorithms

Moussaoui, Mohamed; Zaghdoud, Montaceur; Akaichi, Jalel

doi:10.1109/aiccsa.2015.7507186

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…Generally, these research works are based on connected subgraphs discovered in the network. According to Moussaoui et al, even though the existing approaches for subgraph mining greatly enhanced the mining process, a little attention has been given to the semantic aspects and the uncertainty that may be inherent in real‐world. Many graph matching algorithms perform matching based strictly on link structure (ie, the shape of the graph) and completely ignore the semantics (or meaning) associated with the graph's vertices and edges.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Many efforts have been devoted to extract frequent subgraphs . Generally, the challenge in frequent subgraph mining comes from the costly graph isomorphism that is known to be NP‐complete in their generalization.…”

Section: Related Workmentioning

confidence: 99%