Community detection in networks with positive and negative links

Traag, Vincent; Bruggeman, Jeroen

doi:10.1103/physreve.80.036115

Cited by 377 publications

(313 citation statements)

References 37 publications

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“…Leskovec et al [155] proposed a method to predict the signs of links (positive or negative), yet the prediction of both the existence of a link and its sign has not been well studied. Recent development of social balance theory may provide useful hints [156,157,158].…”

Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,331

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,331

1,621

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“…Social balance theory has also been confronted with large-scale data [5,6], and alternative theories for the interaction of positive/negative relations have also been proposed [6,11]. Focusing on link properties has also been emphasized in the problem of community detection in complex networks [12][13][14][15][16]. This opposes the traditional view of identifying network communities with a set of nodes [17], and it makes it possible for an individual to be assigned to different communities.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of link states in complex networks: The case of a majority rule

et al. 2012

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Motivated by the idea that some characteristics are specific to the relations between individuals and not to the individuals themselves, we study a prototype model for the dynamics of the states of the links in a fixed network of interacting units. Each link in the network can be in one of two equivalent states. A majority link-dynamics rule is implemented, so that in each dynamical step the state of a randomly chosen link is updated to the state of the majority of neighboring links. Nodes can be characterized by a link heterogeneity index, giving a measure of the likelihood of a node to have a link in one of the two states. We consider this link-dynamics model in fully connected networks, square lattices, and Erdös-Renyi random networks. In each case we find and characterize a number of nontrivial asymptotic configurations, as well as some of the mechanisms leading to them and the time evolution of the link heterogeneity index distribution. For a fully connected network and random networks there is a broad distribution of possible asymptotic configurations. Most asymptotic configurations that result from link dynamics have no counterpart under traditional node dynamics in the same topologies.

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“…There are several studies on community partition in social networks with negative (or negatively weighted) edges (Yang et al, 2007;Bansal and Chawla, 2002;Demaine and Immorlica, 2003;Traag and Bruggeman, 2009). Bansal and Chawla (2002) introduced correlation clustering motivated by document clustering and agnostic learning and showed that it is an NP-hard problem to make a partition to a complete signed graph.…”

Section: Related Workmentioning

confidence: 99%

“…Demaine and Immorlica (2003) gave an approximation algorithm based on a linear-programming rounding and the region-growing technique and showed that the problem is APX-hard, i.e., any approximation would require improving the best approximation algorithms known for minimum multi-cut. Traag and Bruggeman (2009) adapted the concept of modularity to detect communities in networks where both positive and negative links are present and also evaluated the social network of international disputes and alliances. However, the algorithm involves several parameters that are hard to configure in practice.…”

Section: Related Workmentioning

confidence: 99%

Examining spectral space of complex networks with positive and negative links

Ying

et al. 2012

IJSNM

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Previous studies on social networks are often focused on networks with only positive relations between individual nodes. As a significant extension, we conduct the spectral analysis on graphs with both positive and negative edges. Specifically, we investigate the impacts of introducing negative edges and examine patterns in the spectral space of the graph's adjacency matrix. Our theoretical results show that communities in a k-balanced signed graph are distinguishable in the spectral space of its signed adjacency matrix even if connections between communities are dense. This is quite different from recent findings on unsigned graphs, where communities tend to mix together in the spectral space when connections between communities become dense. We further conduct theoretical studies based on graph perturbation to examine spectral patterns of general unbalanced signed graphs. We illustrate our theoretical findings with various empirical evaluations on both synthetic data and real world Correlates of War data.

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Community detection in networks with positive and negative links

Cited by 377 publications

References 37 publications

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

Dynamics of link states in complex networks: The case of a majority rule

Examining spectral space of complex networks with positive and negative links

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