Community Mining from Signed Social Networks

By Bo Yang, William Kwok-Wai Cheung, Jiming Liu. IEEE Transactions on Knowledge and Data Engineering, 2007

400 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Signed Network Mining in Social Media (2015, methodology): “A framework based on agent-based random walk model is proposed in [Yang et al. 2007] to extract communities for signed networks.”
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, background): “1) Illustrative signed networks: The two illustrative networks [68] consist of 28 nodes divided into three clusters, where the links within which are positive and between which are negative.”
Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, methodology): “Then, we apply vector-based machine learning algorithms on the node vector representations learned by DNE-SBP, to carry out two important signed network mining tasks, namely, link sign prediction [29]–[31] and community detection [13], [32]–[34].”
The slashdot zoo: mining a social network with negative edges (2009, positive): “Other recent work [28] considers the task of discovering communities from social networks with negative edges.”
Spectral Embedding of Signed Networks (2015, methodology): “[21] proposed an agentbased approach to partition signed networks; Traag and Bruggeman [18] presented a graph clustering method based on modularity using the Potts model representation; Anchuri and Magdon-Ismail [1] extended an existing modularity-based graph-partitioning technique [13] to signed ne”