Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a devic...Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.展开更多
A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to de...A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to detect communities in general networks.Firstly,we construct a bipartite network in accordance with a general network and derive a revised max-flow problem in order to uncover the community structure.Then we present a local heuristic algorithm to find the optimal solution of the revised max-flow problem.This method is applied to a variety of real-world and artificial complex networks,and the partition results confirm its effectiveness and accuracy.展开更多
The max-flow problem in planar networks with only edge capacities has been proved to be in NC (Nickle's Class). This paper considers a more general version of the problem when the nodes as well as the edges have c...The max-flow problem in planar networks with only edge capacities has been proved to be in NC (Nickle's Class). This paper considers a more general version of the problem when the nodes as well as the edges have capacities. In a general network, the node-edge-capacity problem can be easily reduced to the edge-capacity problem. But in the case of planar network this reduction may destroy the planarity, and reduces the problem to the edge-capacity problem in a general network, which is P-complete. A recent contribution presents a new reduction for planar networks, that maintains the planarity. In this paper, it is proved that this reduction is in NC and thus the node-edge-capacity problem in undirected planar networks is in NC. Keywords parallel algorithm - NC (Nickle's Class) algorithm, max-flow Supported by the National Basic Research 973 Program of China under Grant No.G1999032700.展开更多
文摘Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.
基金Supported by the National Natural Science Foundation of China under Grant No.11271006Shandong Provincial Natural Science Foundation under Grant No.ZR2012GQ002
文摘A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to detect communities in general networks.Firstly,we construct a bipartite network in accordance with a general network and derive a revised max-flow problem in order to uncover the community structure.Then we present a local heuristic algorithm to find the optimal solution of the revised max-flow problem.This method is applied to a variety of real-world and artificial complex networks,and the partition results confirm its effectiveness and accuracy.
文摘The max-flow problem in planar networks with only edge capacities has been proved to be in NC (Nickle's Class). This paper considers a more general version of the problem when the nodes as well as the edges have capacities. In a general network, the node-edge-capacity problem can be easily reduced to the edge-capacity problem. But in the case of planar network this reduction may destroy the planarity, and reduces the problem to the edge-capacity problem in a general network, which is P-complete. A recent contribution presents a new reduction for planar networks, that maintains the planarity. In this paper, it is proved that this reduction is in NC and thus the node-edge-capacity problem in undirected planar networks is in NC. Keywords parallel algorithm - NC (Nickle's Class) algorithm, max-flow Supported by the National Basic Research 973 Program of China under Grant No.G1999032700.
