26,361 research outputs found
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv
admin note: text overlap with arXiv:1411.627
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