Community detection algorithms: A comparative analysis

Lancichinetti, Andrea; Fortunato, Santo

doi:10.1103/physreve.80.056117

Cited by 1,836 publications

(1,463 citation statements)

References 54 publications

Supporting

Mentioning

1,415

Contrasting

Unclassified

Order By: Relevance

“…The non-overlapping algorithms used in this study also have very good performance [17]. Blondel greedily maximizes modularity and unfolds a hierarchical community structure with increasing coarseness.…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

Overlapping Communities for Identifying Misbehavior in Network Communications

Moradi

Olovsson

Tsigas

2014

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Abstract. In this paper, we study the problem of identifying misbehaving network communications using community detection algorithms. Recently, it was shown that identifying the communications that do not respect community boundaries is a promising approach for network intrusion detection. However, it was also shown that traditional community detection algorithms are not suitable for this purpose. In this paper, we propose a novel method for enhancing community detection algorithms, and show that contrary to previous work, they provide a good basis for network misbehavior detection. This enhancement extends disjoint communities identified by these algorithms with a layer of auxiliary communities, so that the boundary nodes can belong to several communities. Although non-misbehaving nodes can naturally be in more than one community, we show that the majority of misbehaving nodes belong to multiple overlapping communities, therefore overlapping community detection algorithms can also be deployed for intrusion detection. Finally, we present a framework for anomaly detection which uses community detection as its basis. The framework allows incorporation of application-specific filters to reduce the false positives induced by community detection algorithms. Our framework is validated using large email networks and flow graphs created from real network traffic.

show abstract

Section: Community Detection Algorithmsmentioning

confidence: 99%

Overlapping Communities for Identifying Misbehavior in Network Communications

Moradi

Olovsson

Tsigas

2014

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

show abstract

“…These two community finding algorithms (Infomap and Louvain) are two of the best ones [14]. On the one hand, Infomap uses the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow.…”

Section: Community Prediction Modulementioning

confidence: 99%

BFF: A tool for eliciting tie strength and user communities in social networking services

et al. 2013

View full text Add to dashboard Cite

López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014 Abstract The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.

show abstract

“…The clustering algorithm used is an information theoretic algorithm that models information flows (Rosvall & Bergstrom, 2008). It has shown superior behavior in retrieving clusters from benchmark networks with clusters of a range of different sizes when compared with other popular clustering algorithms (Lancichinetti & Fortunato, 2009). However, any clustering method based on the optimization of a global measure will have an intrinsic resolution limit (Lancichinetti & Fortunato, 2009) such that the clusters retrieved need to be considered with caution.…”

Section: Network Clusteringmentioning

confidence: 99%

The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication

Velden

Lagoze

2013

J Am Soc Inf Sci Tec

View full text Add to dashboard Cite

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field-specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through the examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping subcommunities within a research specialty, and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set to adequately represent a research specialty and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.

show abstract

Community detection algorithms: A comparative analysis

Cited by 1,836 publications

References 54 publications

Overlapping Communities for Identifying Misbehavior in Network Communications

Overlapping Communities for Identifying Misbehavior in Network Communications

BFF: A tool for eliciting tie strength and user communities in social networking services

The extraction of community structures from publication networks to support ethnographic observations of field differences in scientific communication

Contact Info

Product

Resources

About