Community Structure Extraction for Social Networks

2011 ◽  
pp. 266-282
Author(s):  
Helen Hadush ◽  
Gaolin Zheng ◽  
Chung-Hao Chen ◽  
E-Wen Huang
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Spectral Decomposition ◽  
Graph Laplacian ◽  
Network Visualization ◽  
Partition Problem ◽  
Work Community ◽  
Marketing Efficiency ◽  
Balanced Cut ◽  
Structure Extraction

In this work, community structure extraction essentially resorts to its solution to graph partition problem. The authors explore two different approaches. The spectral approach is based on the minimization of balanced cut and its resulting solution comes from the spectral decomposition of the graph Laplacian. The modularity based approach is based on the maximization of modularity and implemented in a hierarchical fashion. In practice, the approach can extract useful information from the community structure, such as what is the most influential component in a given community. Being able to identify and group friends on social networks, the technique can provide a customized advertisement based on their interests. This can have a big return in terms of marketing efficiency. Community structure can also be used for network visualization and navigation. As a result, it can be seen which groups or which pages have more interaction, thus giving a clear image for navigation purposes.

Mathematics of networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Walks ◽  
Adjacency Matrix ◽  
Graph Partitioning ◽  
Dynamic Networks ◽  
Graph Laplacian ◽  
Network Visualization ◽  
Final Part ◽  
Bipartite Networks ◽  
Component Structure ◽  
Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

Direction recovery in undirected social networks based on community structure and popularity

2019 ◽  
Vol 473 ◽  
pp. 31-43 ◽  
Author(s):  
Yi-Ming Wen ◽  
Ling Huang ◽  
Chang-Dong Wang ◽  
Kun-Yu Lin
Keyword(s):  
Social Networks ◽  
Community Structure

scholarly journals Visualizations of personal social networks on Facebook and community structure: an exploratory study

2017 ◽  
Author(s):  
Christina Gkini ◽  
Alexios Brailas
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Single Point ◽  
Community Formation ◽  
Structure Pattern ◽  
Diffusion Of Information ◽  
Qualitative Interpretation ◽  
Network Topologies ◽  
Personal Social Networks ◽  
And Diffusion

We studied the community structure pattern in the visualizations of ten personal social networks on Facebook at a single point in time. It seems to be a strong tendency towards community formation in online personal, social networks: somebody’s friends are usually also friends between them, forming subgroups of more densely connected nodes. Research on community structure in social networks usually focuses on the networks’ statistical properties. There is a need for qualitative studies bridging the gap between network topologies and their sociological implications. To this direction, visual representations of personal networks in social media could be a valuable source of empirical data for qualitative interpretation. Most of the personal social networks’ visualizations in the present study are very highly clustered with densely-knit overlapping subgroups of friends and interconnected between them through wide bridges. This network topology pattern seems to be quite efficient, allowing for a fast spread and diffusion of information across the whole social network.

scholarly journals Semisupervised Community Preserving Network Embedding with Pairwise Constraints

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Dong Liu ◽  
Yan Ru ◽  
Qinpeng Li ◽  
Shibin Wang ◽  
Jianwei Niu
Keyword(s):  
Community Structure ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Nonnegative Matrix ◽  
Machine Learning Algorithms ◽  
Network Visualization ◽  
Network Embedding ◽  
Pairwise Constraints ◽  
Node Clustering ◽  
Low Dimensional

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints (must-link and cannot-link) information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones.

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