Parallel algorithms for anomalous subgraph detection

Zhao, Jieyu; Li, Jianxin; Zhou, Baojian; Chen, Feng; Tomchik, Paul; Ju, Wuyang

doi:10.1002/cpe.3769

Cited by 7 publications

(6 citation statements)

References 20 publications

(37 reference statements)

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“…Another area where parallel algorithms have been developed is for dense subgraph enumeration. There are several implementations for finding maximal cliques in parallel by careful partitioning, pruning and backtracking heuristics [9], [25], [10], [26], [11]. Our results do not extend to the clique enumeration problem.…”

Section: Related Workcontrasting

confidence: 56%

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MIDAS: Multilinear Detection at Scale

Ekanayake

Cadena

Wickramasinghe

et al. 2018

EasyChair Preprints

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We focus on two classes of problems in graph mininghere: (1) finding trees and (2) anomaly detection using networkscan statistics in complex networks. These are fundamentalproblems in a broad class of applications. Most of the parallelalgorithms for such problems are either based on heuristics,which do not scale very well, or use techniques like colorcoding, which have a high memory overhead. In this paper, wedevelop a novel approach for parallelizing both these classesof problems, using an algebraic representation of subgraphsas monomials—this methodology involves detecting multilinearterms in multivariate polynomials. Our algorithms show goodscaling over a large regime, and they run on networks with closeto half a billion edges. The resulting parallel algorithm for treesis able to scale to subgraphs of size18, which has not beenshown before, and it significantly outperforms the best priorcolor coding based method (FASCIA) by more than two ordersof magnitude. Our algorithm for network scan statistics is thefirst such parallelization, and it is able to handle a broad class ofscan statistics functions (both parametric and non-parametric),with the same approach.

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Section: Related Workcontrasting

confidence: 56%

“…Finally, there are only two prior works on parallel graph scan statistics [19], [25]. However, these do not scale to very large instances.…”

Section: Related Workmentioning

confidence: 99%

MIDAS: Multilinear Detection at Scale

Ekanayake

Cadena

Wickramasinghe

et al. 2018

EasyChair Preprints

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“…[ 5 ] demonstrated that by applying an anomaly detection algorithm on call recording logs of a country’s mobile network, they could classify events appearing in a particular time period as emergencies or not. While the majority of the conducted studies are mainly focused on uncovering anomalous vertices, only a handful focus on detecting anomalous communities [ 7 , 13 , 33 , 45 , 48 , 53 , 59 , 70 ].…”

Section: Introductionmentioning

confidence: 99%

Co-Membership-based Generic Anomalous Communities Detection

Lapid

Kagan

Fire

2023

Neural Process Lett

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Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC ), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities’ sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community’s vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

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“…Social network analysis is not limited to sociology or even the social sciences. The relationships between users can be studied in political science, economics, and engineering . However, until very recently, very thin research tradition does not fit into dominant paradigms.…”

Section: Introductionmentioning

confidence: 99%

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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Parallel algorithms for anomalous subgraph detection

Cited by 7 publications

References 20 publications

MIDAS: Multilinear Detection at Scale

MIDAS: Multilinear Detection at Scale

Co-Membership-based Generic Anomalous Communities Detection

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

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