Publications (5)0.74 Total impact
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ABSTRACT: We consider the problem of inferring the opinions of a social network through strategically sampling a minimum subset of nodes by exploiting correlations in node opinions. We first introduce the concept of information dominating set (IDS). A subset of nodes in a given network is an IDS if knowing the opinions of nodes in this subset is sufficient to infer the opinion of the entire network. We focus on two fundamental algorithmic problems: (i) given a subset of the network, how to determine whether it is an IDS; (ii) how to construct a minimum IDS. Assuming binary opinions and the local majority rule for opinion correlation, we show that the first problem is coNPcomplete and the second problem is NPhard in general networks. We then focus on networks with special structures, in particular, acyclic networks. We show that in acyclic networks, both problems admit linearcomplexity solutions by establishing a connection between the IDS problems and the vertex cover problem. Our technique for establishing the hardness of the IDS problems is based on a novel graph transformation that transforms the IDS problems in a general network to that in an odddegree network. This graph transformation technique not only gives an approximation algorithm to the IDS problems, but also provides a useful tool for general studies related to the local majority rule. Besides opinion sampling for applications such as political polling and market survey, the concept of IDS and the results obtained in this paper also find applications in data compression and identifying critical nodes in information networks.05/2014; 
Article: The Thinnest Path Problem
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ABSTRACT: We formulate and study the thinnest path problem in wireless ad hoc networks. The objective is to find a path from a source to its destination that results in the minimum number of nodes overhearing the message by a judicious choice of relaying nodes and their corresponding transmission power. We adopt a directed hypergraph model of the problem and establish the NPcompleteness of the problem in 2D networks. We then develop two polynomialtime approximation algorithms that offer $\sqrt{\frac{n}{2}}$ and $\frac{n}{2\sqrt{n1}}$ approximation ratios for general directed hypergraphs (which can model nonisomorphic signal propagation in space) and constant approximation ratios for ring hypergraphs (which result from isomorphic signal propagation). We also consider the thinnest path problem in 1D networks and 1D networks embedded in 2D field of eavesdroppers with arbitrary unknown locations (the socalled 1.5D networks). We propose a linearcomplexity algorithm based on nested backward induction that obtains the optimal solution to both 1D and 1.5D networks. This algorithm does not require the knowledge of eavesdropper locations and achieves the best performance offered by any algorithm that assumes complete location information of the eavesdroppers.05/2013;  [Show abstract] [Hide abstract]
ABSTRACT: A hypergraph is a set V of vertices and a set of nonempty subsets of V, called hyperedges. Unlike graphs, hypergraphs can capture higherorder interactions in social and communication networks that go beyond a simple union of pairwise relationships. In this paper, we consider the shortest path problem in hypergraphs. We develop two algorithms for finding and maintaining the shortest hyperpaths in a dynamic network with both weight and topological changes. These two algorithms are the first to address the fully dynamic shortest path problem in a general hypergraph. They complement each other by partitioning the application space based on the nature of the change dynamics and the type of the hypergraph.01/2012; 
Conference Paper: The thinnest path problem for secure communications: A directed hypergraph approach
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ABSTRACT: We formulate and study the thinnest path problem in wireless ad hoc networks. The objective is to find a path from the source to the destination that results in the minimum number of nodes overhearing the message by carefully choosing the relaying nodes and their corresponding transmission power. We adopt a directed hypergraph model of the problem and establish the NPcompleteness of the problem in 2D networks. We then develop a polynomialtime approximation algorithm that offers a √n/2 approximation ratio for general directed hypergraphs (which can model nonisomorphic signal propagation in space) and constant approximation ratio for disk hypergraphs (which result from isomorphic signal propagation). We also consider the thinnest path problem in 1D networks and 1D networks embedded in 2D field of eavesdroppers with arbitrary unknown locations (the socalled 1.5D networks). We propose a linearcomplexity algorithm based on nested backward induction that obtains the optimal solution to both 1D and 1.5D networks. In particular, no algorithm, even with the complete knowledge of the locations of the eavesdroppers, can obtain a thinner path than the proposed algorithm which does not require the knowledge of eavesdropper locations.Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on; 01/2012 
Conference Paper: Broadcasting in MultiRadio MultiChannel Wireless Networks using Simplicial Complexes.
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ABSTRACT: We consider the broadcasting problem in multiradio multichannel ad hoc networks. The objective is to minimize the total cost of the networkwide broadcast, where the cost can be of any form that is summable over all the transmissions (e.g., the transmission and reception energy, the price for accessing a specific channel). Our technical approach is based on a simplicial complex model that allows us to capture the broadcast nature of the wireless medium and the heterogeneity across radios and channels. Specifically, we show that broadcasting in multiradio multichannel ad hoc networks can be formulated as a minimum spanning problem in simplicial complexes. We establish the NPcompleteness of the minimum spanning problem and propose two approximation algorithms with orderoptimal performance guarantee. The first approximation algorithm converts the minimum spanning problem in simplical complexes to a minimum connected set cover (MCSC) problem. The second algorithm converts it to a nodeweighted Steiner tree problem under the classic graph model. These two algorithms offer tradeoffs between performance and timecomplexity. In a broader context, this work appears to be the first that studies the minimum spanning problem in simplicial complexes and weighted MCSC problem.IEEE 8th International Conference on Mobile Adhoc and Sensor Systems, MASS 2011, Valencia, Spain, October 1722, 2011; 01/2011
Publication Stats
2  Citations  
0.74  Total Impact Points  
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Institutions

2011

University of California, Davis
 Department of Electrical and Computer Engineering
Davis, California, United States
