Search in power-law networks

2010

The resource distribution in P2P network has an obvious scale free character. Using this inherent character to design resource search strategy is great significant for improving searching efficiency and reducing the costs. We analyze the scale free distribution character in P2P network, and propose a reliable random walk search algorithm to achieve high and reliable search efficiency through forwarding query messages based on the P2P scale free distribution. Moreover, we design simulation experiments to evaluate the performance of reliable random walk. The experimental results show that the reliable random walk is a scalable resource searching algorithm with high search efficiency and low costs.

Computing Research Repository, 2002

We review a number of message-passing algorithms that can be used to search through power-law networks. Most of these algorithms are meant to be improvements for peer-to-peer file sharing systems, and some may also shed some light on how unstructured social networks with certain topologies might function relatively efficiently with local information. Like the networks that they are designed for,

Lecture Notes in Computer Science, 2008

In this paper, we report a novel and efficient algorithm for searching P2P networks having a power law topology. Inspired by the natural immune system, it is a completely decentralized algorithm where each peer searches by sending out random walkers to a limited number of neighbors. As it finds other peers having similar content, it restructures its own neighborhood with the objective of bringing them closer. This restructuring leads to clustering of nodes with similar content, thus forming P2P communities. Alongside, the search algorithm also adapts its walk strategy in order to take advantage of the community thus formed. This search strategy is more than twice as efficient as pure random walk on the same network.

International Journal of Parallel, Emergent and Distributed Systems, 2010

We propose a new mechanism for generating networks with a wide variety of degree distributions. The idea is a modification of the well-studied preferential attachment scheme in which the degree of each node is used to determine its evolving connectivity. Modifications to this base protocol to include features other than connectivity have been considered in building the network. However, some of the existing models are merely formulaic and do not offer an explanation that can be interpreted naturally or intuitively, while the others require certain information that is not available in many real-world circumstances. We propose instead a protocol based only on a single statistical feature which results from the reasonable assumption that the effect of various attributes, which determine the ability of each node to attract other nodes, is multiplicative. This composite attribute or fitness is lognormally distributed and is used in forming the complex network. We show that, by varying the parameters of the lognormal distribution, we can recover both exponential and power-law degree distributions. The exponents for the power-law case are in the correct range seen in real-world networks. Further, as power-law networks with exponents in the same range are a crucial ingredient of efficient search algorithms in P2P networks, we believe our network construct may serve as a basis for new protocols that will enable P2P networks to efficiently establish a topology conducive to optimised search procedures.

2005

This paper presents an analytical framework to study search strategies in large-scale decentralized unstructured peer-to-peer (P2P) networks. The peers comprising the P2P network and their application-level connections are modeled as generalized random graphs (GRGs) whose simple and efficient analysis is accomplished using the generating function of the graph's degree distribution.

Proceedings IEEE INFOCOM 2005. 24th Annual …, 2005

Pdpta, 2006

Scalability in a peer-to-peer network is a challenging problem. Unstructured peer-to-peer networks inherently lack scalability, and structured networks are inefficient for a high churn rate. In this paper, we present a scalable search algorithm for a decentralized unstructured peer-to-peer network using a method to dynamically determine the number of nodes to forward a query to at once. The decision is based on the degree to which each neighbor has contributed to previous successful searches. The algorithm automatically creates a spanning graph of the high traffic links. Once a stable spanning graph is created, a query tends to travel along the edges of the spanning graph. This way, the number of hops required for a search is roughly bound by the diameter of the spanning graph. The simulation shows that our algorithm demonstrates significantly better performance in terms of the number of messages generated and hops required for a search over other popular algorithms.

Physical Review E, 2005

This paper develops a framework for analyzing and designing dynamic networks comprising different classes of nodes that coexist and interact in one shared environment. We consider ad hoc (i.e., nodes can leave the network unannounced, and no node has any global knowledge about the class identities of other nodes) preferentially grown networks, where different classes of nodes are characterized by different sets of local parameters used in the stochastic dynamics that all nodes in the network execute. We show that multiple scale-free structures, one within each class of nodes, and with tunable power-law exponents (as determined by the sets of parameters characterizing each class) emerge naturally in our model. Moreover, the coexistence of the scale-free structures of the different classes of nodes can be captured by succinct phase diagrams, which show a rich set of structures, including stable regions where different classes coexist in heavy-tailed (i.e., exponent is between 2 and 3) and light-tailed (i.e., exponent is > 3) states, and sharp phase transitions. The topology of the emergent networks is also shown to display a complex structure, akin to the distribution of different components of an alloyed material; e.g., nodes with a light-tailed scale-free structure get embedded to the outside of the network, and have most of its edges connected to nodes belonging to the class with a heavy-tailed distribution. Finally, we show how the dynamics formulated in this paper will serve as an essential part of ad-hoc networking protocols, which can lead to the formation of robust and efficiently searchable networks (including, the well-known Peer-To-Peer (P2P) networks) even under very dynamic conditions.

Knowledge and Information Systems, 2014

Borrowing from concepts in expander graphs, we study the expansion properties of real-world, complex networks (e.g. social networks, unstructured peer-to-peer or P2P networks) and the extent to which these properties can be exploited to understand and address the problem of decentralized search. We first produce samples that concisely capture the overall expansion properties of an entire network, which we collectively refer to as the expansion signature. Using these signatures, we find a correspondence between the magnitude of maximum expansion and the extent to which a network can be efficiently searched. We further find evidence that standard graph-theoretic measures, such as average path length, fail to fully explain the level of "searchability" or ease of information diffusion and dissemination in a network. Finally, we demonstrate that this high expansion can be leveraged to facilitate decentralized search in networks and show that an expansion-based search strategy outperforms typical search methods.

Computing Research Repository - CORR, 2006

Decentralized search aims to find the target node in a large network by using only local information. The applications of it include peer-to-peer file sharing, web search and anything else that requires locating a specific target in a complex system. In this paper, we examine the degree-based decentralized search method. Specifically, we evaluate the efficiency of the method in different cases with different amounts of available local information. In addition, we propose a simple refinement algorithm for significantly shortening the length of the route that has been found. Some insights useful for the future developments of efficient decentralized search schemes have been achieved. Index Terms—Decentralized search, complex network, scale-free network.