What do real communities in social networks look like? Community detection plays a key role in understanding the structure of reallife graphs with impact on recommendation systems, load balancing and routing. Previous community detection methods look for uniform blocks in adjacency matrices. However, after studying four real networks with ground-truth communities, we provide empirical evidence ...

The IT infrastructure of today needs to be ready to defend against massive cyber-attacks which often originate from distributed attackers such as Botnets. Most Intrusion Detection Systems (IDSs), nonetheless, are still working in isolation and cannot effectively detect distributed attacks. Collaborative IDSs (CIDSs) have been proposed as a collaborative defense against the ever more sophisticat...

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genom...

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and sc...

The Louvain method [1] is a successful technique for community detection that decomposes a network by optimizing the modularity of the partitions. It was not designed, however, for cases where a network is incomplete and network nodes contain some unused information. A new method is introduced here, Propagation Mergence (PM), that was designed to handle incomplete networks, and leverage node in...

Nowadays, many new social networks offering specific services spring up overnight. In this paper, we want to detect communities for emerging networks. Community detection for emerging networks is very challenging as information in emerging networks is usually too sparse for traditional methods to calculate effective closeness scores among users and achieve good community detection results. Mean...

Community detection is a fundamental problem in network science, which has attracted much attention in the past several decades, especially in the social network area. Lots of studies about detecting communities in the static networks have been proposed in the literature, which could be found in survey [1] and method [2]. Real-world networks, especially most of the social networks, however, are...

We formalize the problem of detecting a community in a network into testing whether in a given (random) graph there is a subgraph that is unusually dense. We observe an undirected and unweighted graph on N nodes. Under the null hypothesis, the graph is a realization of an Erdös-Rényi graph with probability p0. Under the (composite) alternative, there is a subgraph of n nodes where the probabili...

In this paper we apply theoretical and practical results from facility location theory to the problem of community detection in networks. The result is an algorithm that computes bounds on a minimization variant of local modularity. We also define the concept of an edge support and a new measure of the goodness of community structures with respect to this concept. We present preliminary results...

Detecting the underlying community structure of networks is an important problem in complex network analysis. Modularity is a well-known quality function introduced by Newman, that measures how vertices in a community share more edges than what would be expected in a randomized network. However, this limited view on vertex similarity leads to limits in what can be resolved by modularity. To ove...