Markov clustering is an effective unsupervised pattern recognition algorithm for data clustering in high-dimensional feature space. However, its community detection performance in complex networks has been dem...
We provide a novel family of generative block-models for random graphs that naturally incorporates degree distributions: the block-constrained configuration model. Block-constrained configuration models build ...
The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks...
It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through rece...
This paper examines the process of protest claim-making by reconstructing the semantic structure of online communication that took place prior to the first street event of a protest. Topic networks are identif...
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circum...
We introduce the random intersection graph with communities, a new model for networks with overlapping communities with arbitrary internal structure. We construct the model from a list of arbitrary community grap...
In this paper it is analysed how community formation in an adaptive network for bonding based on similarity (homophily) can be related to characteristics of the adaptive network’s structure, which includes the...
We present a model for network transformation mediated by confinement, as a demonstration of a simple network dynamics that has a direct connection with real world quantities. The model has the capacity of gen...
This paper addresses two critical questions related to (1) the co-evolutionary dynamics between information diffusion and network topology and (2) the relationship between viral content and social reinforcemen...
The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social...
Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind c...
In real world complex networks, communities are usually both overlapping and hierarchical. A very important class of complex networks is the bipartite networks. Maximal bicliques are the strongest possible str...
We have investigated community structure in the co-inventor network of a given cohort of patents and related this structure to the dynamics of how these patents acquire their first citation. A statistically si...
Social networks often has the graph structure of giant strongly connected component (GSCC) and its upstream and downstream portions (IN and OUT), known as a bow-tie structure since a pioneering study on the Wo...
The use of networks to analyze biological data, such as large gene or protein expression datasets, is on the rise. Often, there is an interest of identifying modules (or communities) of biological molecules th...
We deal with the problem of modeling and characterizing the community structure of complex systems. First, we propose a mathematical model for directed temporal networks based on the paradigm of activity drive...
As recent work demonstrated, the task of identifying communities in networks can be considered analogous to the classical problem of decoding messages transmitted along a noisy channel. We leverage this analog...
This paper studies the driving forces behind the formation of ties within the major communities in the Japanese nationwide network of production, which contains one million firms and five million links between...