Finding overlapping communities based on Markov chain and link clustering

Since community structure is an important feature of complex network, the study of community detection has attracted more and more attention in recent years. Despite most researchers focus on identifying disjoint communities, communities in many real networks often overlap. In this paper, we proposed a novel MCLC algorithm to discover overlapping communities, which using random walk on the line graph and attraction intensity. Unlike traditional random walk starting from a node, our random walk starts from a link. First we transform an undirected network graph to a weighted line graph, and then random walks on this line graph can be associated with a Markov chain. By calculating the transition probability of the Markov chain, we obtain the similarity between link pairs. Next the links can be clustered into “link communities” by a linkage method, and these nodes between link communities can be overlapping nodes. When converting the “link communities” into the “node communities”, we make a definition of attraction intensity to control the overlapping size. Finally the detected communities are permitted overlapped. Experiments on synthetic networks and some real world networks validate the effectiveness and efficiency of the proposed algorithm. Comparing overlapping modularity Qov with other related algorithms, the results of this algorithm are satisfactory

A weighted network model based on the correlation degree between nodes

Many complex networks in practice can be described by weighted network models, and the BBV model is one of the most classical ones. In this paper, by introducing the concept of correlation degree between nodes, a new weighted network model based on the BBV model is proposed. The model takes the both node strength and node correlation into consideration during the network evolution, which better reveals the evolving mechanisms behind various real-world networks. Results from theoretical analysis and numerical simulation have demonstrated the scale-free property and small-world property of the network model, which have been widely observed in many real-world networks. Compared with the BBV model, the added correlation preferential attachment rule in the model leads to a faster network propagation velocity.Location : Shenzhen, ChinaDate : 16-18 December 201

QoS-Aware and Load-Balance Routing for IEEE 802.11s Based Neighborhood Area Network in Smart Grid

Monitoring and transforming smart grid (SG) assets in a timely manner is highly desired for emerging smart grid applications. This critically requires the design of a neighborhood area network (NAN) which is capable of providing high-efficiency and reliable two-way last mile communication from meters to other SG domains. For this demand, IEEE 802.11s based wireless mesh network (WMN) is anticipated to be utilized in a NAN as it can provide high scalability, high-speed and cost-effective wireless transmission. In this paper, we propose a NAN QoS-aware and load-balance routing scheme (NQA-LB) based on the default hybrid wireless mesh protocol (HWMP) of IEEE 802.11s, which aims to address multiple QoS requirements from different NAN applications, and guarantee the highly reliability transfer of NAN traffic data towards gateway. With the NQA-LB, various QoS requirements can be satisfied through sufficient differentiated services as well as network congestion is mitigated by achieving load balance between multiple transmission paths. In order to improve the reliability of NQA-LB, we present an EDCA based adaptive priority adjustment scheme, called AP-EDCA, which dynamically adjusts packet’s priority to increase the throughput under low load condition and to mitigate the collision under heavy load condition to improve the reliability of applications with high QoS requirements. Extensive simulation experiments demonstrate the superiority of the proposed scheme in terms of packet delivery ratio, end-to-end delay and throughput while satisfies various QoS requirements much better at the same time

Performance Optimization in UAV-Assisted Wireless Powered mmWave Networks for Emergency Communications

In this paper, we explore how a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial millimeter wave (mmWave) base station to provide recharging service and radio access service in a postdisaster area with unknown user distribution. The addressed optimization problem is to find out the optimal path starting and ending at the same recharging point to cover a wider area under limited battery capacity, and it can be transformed to an extended multiarmed bandit (MAB) problem. We propose the two improved path planning algorithms to solve this optimization problem, which can improve the ability to explore the unknown user distribution. Simulation results show that, in terms of the total number of served user equipment (UE), the number of visited grids, the amount of data, the average throughput, and the battery capacity utilization level, one of our algorithms is superior to its corresponding comparison algorithm, while our other algorithm is superior to its corresponding comparison algorithm in terms of the number of visited grids