Identity and Search in Social Networks

Social networks have the surprising property of being "searchable": Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.Comment: 4 page, 3 figures, revte

A Secure and User Privacy-Preserving Searching Protocol for Peer-to-Peer Networks

File sharing peer-to-peer networks have become quite popular of late as a new paradigm for information exchange among large number of users in the Internet. However, these networks suffer from several problems such as fake content distribution, free riding, whitewashing, poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. In this paper, a secure and efficient searching scheme for peer-to-peer networks has been proposed that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures among the trustworthy peers, the scheme provides a highly reliable module for protecting the privacy of the users and data in the network. Simulation results have demonstrated that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times

A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201

Architecture analysis of peer-to-peer network structure and data exhanges for distribution of contraband material.

Because of the anonymity that P2P networks provide, they are an ideal medium for the exchange of contraband material such as child pornography. Unfortunately, not much research has been conducted on how to best monitor these types of networks for contraband searching and sharing activity. This thesis proposes techniques to advance the state of the art in peer to peer data exchange monitoring and detection of nodes that participate in distributing and sharing contraband material. Because of the legal considerations in working with a live P2P network and the technical di culty in developing and testing a surveillance system for P2P networks, a simulator was developed that attempts to accurately simulate the behavior of users on P2P networks based upon empirical data collected from several researchers. With the help of the simulation platform that has been developed, a complete methodology for monitoring contraband activity and reporting the most proli c contraband users has been created. This methodology, if implemented on an actual P2P network, should allow the detection of members of the network who are the most active sharers and distributors of contraband material

QoS routing in ad-hoc networks using GA and multi-objective optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version