Introduction
Social computing
Definition of social computing
Research fields of social computing
Social science-oriented social computing
Application-oriented social computing
Soft computing
Fuzzy logic
Formal concept analysis
Rough set theory
Soft set theory
When social computing meets soft computing
Representation of social networks
Fuzzy logic based representation approach
FCA based representation approach
Positional analysis
Topological structure analysis
FL-based topological structure mining
FCA-based topological structure mining
RST-based topological structure mining
Social web mining
Social data analysis
Medicine and healthcare services
Relevant softwares for social computing
No | General software packages | Specialized software packages |
---|---|---|
Type | Academic/free | Academic/free |
1 | Agna Applied graph and network analysis | Blanche Network dynamics |
2 | DyNet (SE and LS) [91] Data-driven visualizations | CID-ABM Competing idea diffusion agent based model |
3 | GUESS The graph exploration system | CFinder [92] Finding and visualizing dense groups |
4 | Pajek [93] Program for large network analysis | Commetrix [94] Dynamic network visualization and analysis |
5 | NodeXL [95]: viewing and analyzing network graphs | PGRAPH [96]: Kinship networks |
6 | igraph (R, Python, C) [97] Creating and manipulating graphs | SONIVIS Analyzing and visualizing virtual information space |
7 | NetVis [98]: dynamic visualization of social networks | E-Net [99]: Ego-NETwork analysis |
8 | ORA [100]: dynamic network analysis | EgoNet [101]: egocentric networks |
9 | SocNetV: social networks visualiser | KeyPlayer [102]: identifying nodes |
10 | UCINET 6 [103] Comprehensive social network analysis software | KliqFinder: cohesive subgroups |
11 | visone: analyis and visualization of social networks | Network genie: network surveys |
12 | JUNG (Java): Java Universal Network/graph framework | PNet: exponential random graph models (ERGMs) |
13 | libSNA (Python) Open-source library for social network analysis | SONIVIS Analyzing and visualizing virtual information spaces |
14 | NetworkX (Python) [104]: package for complex networks | StOCNET [105]: statistical analysis |
No | Software name | Descriptions |
---|---|---|
1 | aiSee [106] | Graph visualization |
2 | Apache Agora [107] | Visualizing virtual communities |
3 | Cytoscape [108] | Visualizing molecular interaction networks |
4 | Gephi [109] | Visualization and exploration platform |
5 | Graphviz [110] | Graph visualization |
6 | KrackPlot [111] | Social network visualization program |
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Social computing issues In this step, we can execute the topological structure mining and analysis based on the above extracted formal concepts. For example, our previous work [29] has proved that the equivalence relation between the equiconcepts and cliques. With this relation, we can extract the k-cliques, k-clique communities from social networks. Interestingly, the location-focused communities detection and evolutionary can be accomplished by observing the changing patterns of m-triadic concepts [113]. In field of graph matching, the formal concepts are regarded as the main features of the graphs for further evaluating the similarity between graphs [68].