Controversy on whether local (deterministic) or regional (stochastic) factors control the structure of communities persists after decades of research. The main reason for why it has not been resolved may lie in the nature of evidence which largely comes from realized natural communities. In such communities assembly history leaves a mark that may support either set of factors. To avoid the conf...

Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This ...

Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is...

The study of complex networks [1, 2, 3] has recently arisen as a powerful tool for understanding a variety of systems, such as biological and social interactions [4, 5], technology communications and interdependencies [1, 6], and many others. The problem of detecting communities, subsets of network nodes that are densely connected amongst themselves while being sparsely connected to other nodes...

We propose a novel approach called the Local Lanczos Spectral Approximation (LLSA) for identifying all latent members of a local community from very few seed members. To reduce the computation complexity, we first apply a fast heat kernel diffusing to sample a comparatively small subgraph covering almost all possible community members around the seeds. Then starting from a normalized indicator ...

Community structure is an important aspect of network analysis, with a variety of reallife applications. Local community detection algorithms, which are relatively new in literature, provide the opportunity to analyze community structure in large networks without needing global information. We focus our work on a state-of-the-art algorithm developed by Yang and Leskovec and evaluate it on three...

Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics. For increasingly common large network data sets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e. methods that identify all latent members of a particular community from a few lab...

Multi-scale (“multiresolution”) community detection attempts to identify the most relevant divisions (groups of related nodes) of an arbitrary network over a range of network scales. This task is generally accomplished by analyzing community stability in an average sense across all communities in the network. In some systems, contending partitions of the global community structure may be vague ...

The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node...

One of the most important problems in the field of social network analysis, and one of the most discussed ones, is community detection, aimed at clustering the nodes on the basis of their social relationships. Community detection is relevant in various fields, including: recommendation systems, link prediction and suggestion, epidemic spreading and information diffusion, sybil detection. In thi...