A review of community detection algorithms in signed social networks

Ruby,; Kaur, Inderjeet

doi:10.1109/icecds.2017.8390199

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Baseline signed graph theory was used to explain the relative status that individuals hold within in a social network (Leskovec et al 2010a, b) and focused on socially-conscious science to help understand bias, controversy, conflict, and trust (Guha et al 2004;Mishra and Bhattacharya 2011). All mathematical models in network science that model intents and trends in online social networks have relied on aspects of well-established consensus-based models in signed graph theory (Chen et al 2018;Garimella et al 2017;Yuan et al 2017) and balanced modeling (Javed et al 2018;Lu and Zhou 2011;Ruby and Kaur 2017;Tang et al 2016;Zhao et al 2018;Zhou et al 2018).…”

Section: Related Work In Social Network Analysis and Cyberneticsmentioning

confidence: 99%

Characterizing attitudinal network graphs through frustration cloud

Rusnak

Tešić

2021

Data Min Knowl Disc

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Attitudinal network graphs are signed graphs where edges capture an expressed opinion; two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). A signed graph is called balanced if each of its cycles includes an even number of negative edges. Balance is often characterized by the frustration index or by finding a single convergent balanced state of network consensus. In this paper, we propose to expand the measures of consensus from a single balanced state associated with the frustration index to the set of nearest balanced states. We introduce the frustration cloud as a set of all nearest balanced states and use a graph-balancing algorithm to find all nearest balanced states in a deterministic way. Computational concerns are addressed by measuring consensus probabilistically, and we introduce new vertex and edge metrics to quantify status, agreement, and influence. We also introduce a new global measure of controversy for a given signed graph and show that vertex status is a zero-sum game in the signed network. We propose an efficient scalable algorithm for calculating frustration cloud-based measures in social network and survey data of up to 80,000 vertices and half-a-million edges. We also demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.

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Section: Related Work In Social Network Analysis and Cyberneticsmentioning

confidence: 99%

Characterizing attitudinal network graphs through frustration cloud

Rusnak

Tešić

2021

Data Min Knowl Disc

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“…Baseline signed graph theory was used to explain relative status individuals hold within in a social network [28,29] and focused on socially-conscious science to help understand bias, controversy, conflict, and trust [16,34]. All mathematical models in the network science that model intents and trends in online social networks have relied on aspects of well established consensus-based models in signed graph theory [10,14,51] and balanced modeling [24,33,40,46,53,54].…”

Section: Related Workmentioning

confidence: 99%

Characterizing Attitudinal Network Graphs through Frustration Cloud

Rusnak,

Tešić

2020

Preprint

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Attitudinal Network Graphs (ANG) are network graphs where edges capture an expressed opinion: two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). Measure of consensus in attitudinal graph reflects how easy or difficult consensus can be reached that is acceptable by everyone. Frustration index is one such measure as it determines the distance of a network from a state of total structural balance. In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states for a given network. The frustration cloud resolves the consensus problem with minimal sentiment disruption, taking all possible consensus views over the entire network into consideration. A frustration cloud based approach removes the brittleness of traditional network graph analysis, as it allows one to examine the consensus on entire graph. A spanning-tree-based balancing algorithm captures the variations of balanced states and global consensus of the network, and enables us to measure vertex influence on consensus and strength of its expressed attitudes. The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and survey data. We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery, successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions.

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A review of community detection algorithms in signed social networks

Cited by 2 publications

References 10 publications

Characterizing attitudinal network graphs through frustration cloud

Characterizing attitudinal network graphs through frustration cloud

Characterizing Attitudinal Network Graphs through Frustration Cloud

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