A tremendous volume of personal and business e mail is generated daily. Some of these e mail messages may eventually have considerable economic value, such as those sent by United States presidents or by famous authors. Others may have significant sentimental value to family members when the...

We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in...

Nowadays finding patterns in large social network datasets is a growing challenge and an important subject of interest. One of current problems in this field is identifying clusters within social networks with large number of nodes. Social network clusters are not necessarily disjoint sets;...

We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than equality of agents. We show that if links between similar...

We propose a random network model incorporating heterogeneity of agents and a continuous notion of homophily. Unlike the vast majority of the corresponding economic literature, we capture homophily in terms of similarity rather than equality by assuming that the probability of linkage between...

The communities in a network have distinct characteristics and interrelationships. Community discovery methods based on node embedding and deep learning have surpassed spectral clustering and statistical inference as the preferred methods for handling high-dimensional network data. Community...