Topic
Network science
About: Network science is a research topic. Over the lifetime, 4128 publications have been published within this topic receiving 252914 citations.
Papers published on a yearly basis
1 / 2
Papers
16 May 2016
TL;DR: The proposed CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks and is called cross-layer betweenness centrality (CBC).
Abstract: Several real-life social systems witness the presence of multiple interaction types (or layers) among the entities, thus establishing a collection of co-evolving networks, known as multiplex networks. More recently, there has been a significant interest in developing certain centrality measures in multiplex networks to understand the influential power of the entities (to be referred as vertices or nodes hereafter). In this paper, we consider the problem of studying how frequently the nodes occur on the shortest paths between other nodes in the multiplex networks. As opposed to simplex networks, the shortest paths between nodes can possibly traverse through multiple layers in multiplex networks. Motivated by this phenomenon, we propose a new metric to address the above problem and we call this new metric cross-layer betweenness centrality (CBC). Our definition of CBC measure takes into account the interplay among multiple layers in determining the shortest paths in multiplex networks. We propose an efficient algorithm to compute CBC and show that it runs much faster than the naive computation of this measure. We show the efficacy of the proposed algorithm using thorough experimentation on two real-world multiplex networks. We further demonstrate the practical utility of CBC by applying it in the following three application contexts: discovering non-overlapping community structure in multiplex networks, identifying interdisciplinary researchers from a multiplex co-authorship network, and the initiator selection for message spreading. In all these application scenarios, the respective solution methods based on the proposed CBC are found to be significantly better performing than that of the corresponding benchmark approaches.
28 citations
TL;DR: The notion of self-organizing agent networks is introduced, and an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities is provided, which utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network.
Abstract: A network community refers to a special type of network structure that contains a group of nodes connected based on certain relationships or similar properties. Our ability to mine communities hidden inside networks will readily enable us to effectively understand and exploit such networks. So far, various methods and algorithms have been developed to perform the task of community mining, where it is often required that the networks are processed in a centralized manner, and their structures will not dynamically change. However, in the real world, many applications involve distributed and dynamically evolving networks, in which resources and controls are not only decentralized but also updated frequently. It would be difficult for the existing methods to deal with these types of networks since their global topological representations are either not available or too hard to obtain due to their huge size, decentralization, and/or dynamic updates. The aim of our work is to address the problem of mining communities from a distributed and dynamic network. It differs from the previous ones in that here we introduce the notion of self-organizing agent networks, and provide an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities. The AOC-based method utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network, based only on their local views and interactions. While providing detailed formulations, we present the results of our systematic validations using real-world benchmark networks as well as synthetic networks that include a distributed intelligent Portable Digital Assistant (iPDA) network example.
28 citations
23 Oct 2010
TL;DR: A distributed method in solving subgraph mining problems with the help of MapReduce, which is an efficient method of computing and it is a better solution of sub graph mining in extreme large graphs.
Abstract: Graph mining plays an important part in the researches of data mining, and it is widely used in biology, physics, telecommunications and Internet in recently emerging network science. Subgraph mining is a main task in this area, and it has attracted much interest. However, with the growth of graph datasets, most of these former works which mainly rely on single chip computational capacity, cannot process massive graphs. In this paper, we propose a distributed method in solving subgraph mining problems with the help of MapReduce, which is an efficient method of computing. The candidate subgraphs are reduced efficiently according to the degrees of nodes in graphs. The results of our research show that the algorithm is efficient and scalable, and it is a better solution of subgraph mining in extreme large graphs.
28 citations
TL;DR: In this paper, a node-based method combined with a composition and rejection algorithm is proposed to circumvent the complexity of the average case complexity of O [ log ( log N ) ] per update for general networks.
28 citations
TL;DR: This article presents a proposal for the efficient solution to one of the most frequently requested services on social networks; namely, taking different types of relationships into account in order to locate a particular member of the network.
Abstract: One of the most important types of applications currently being used to share knowledge across the Internet are social networks. In addition to their use in social, professional and organizational spheres, social networks are also frequently utilized by researchers in the social sciences, particularly in anthropology and social psychology. In order to obtain information related to a particular social network, analytical techniques are employed to represent the network as a graph, where each node is a distinct member of the network and each edge is a particular type of relationship between members including, for example, kinship or friendship. This article presents a proposal for the efficient solution to one of the most frequently requested services on social networks; namely, taking different types of relationships into account in order to locate a particular member of the network. The solution is based on a biologically-inspired modification of the ant colony optimization algorithm.
28 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 10 |
| 2024 | 27 |
| 2023 | 79 |
| 2022 | 136 |
| 2021 | 236 |
| 2020 | 280 |