Showing papers by "My T. Thai published in 2013"

Detecting Critical Nodes in Interdependent Power Networks for Vulnerability Assessment

Abstract: Power networks and information systems become more and more interdependent to ensure better supports for the functionality as well as improve the economy. However, power networks also tend to be more vulnerable due to the cascading failures from their interdependent information systems, i.e., the failures in the information systems can cause the failures of the coupled portion in power networks. Therefore, the accurate vulnerability assessment of interdependent power networks is of great importance in the presence of unexpected disruptive events or adversarial attacks targeting on critical network nodes. In this paper, we study the Interdependent Power Network Disruptor (IPND) optimization problem to identify critical nodes in an interdependent power network whose removals maximally destroy its functions due to both malfunction of these nodes and the cascading failures of its interdependent communication network. First, we show the IPND problem is NP-hard to be approximated within the factor of (2-e) . Despite its intractability, we propose a greedy framework with novel centrality functions based on the networks' interdependencies, to efficiently solve this problem in a timely manner. An extensive experiment not only illustrates the effectiveness of our approach on networks with different topologies and interdependencies, but also highlights some important observations which help to sharpen the robustness of interdependent networks in the future.

Maximizing the Spread of Positive Influence in Online Social Networks

Abstract: Online social networks (OSNs) provide a new platform for product promotion and advertisement. Influence maximization problem arisen in viral marketing has received a lot of attentions recently. Most of the existing diffusion models rely on one fundamental assumption that an influenced user necessarily adopts the product and encourages his/her friends to further adopt it. However, an influenced user may be just aware of the product. Due to personal preference, neutral or negative opinion can be generated so that product adoption is uncertain. Maximizing the total number of influenced users is not the uppermost concern, instead, letting more activated users hold positive opinions is of first importance. Motivated by above phenomenon, we proposed a model, called Opinion-based Cascading (OC) model. We formulate an opinion maximization problem on the new model to take individual opinion into consideration as well as capture the change of opinions at the same time. We show that under the OC model, opinion maximization is NP-hard and the objective function is no longer submodular. We further prove that there does not exist any approximation algorithm with finite ratio unless P=NP. We have designed an efficient algorithm to compute the total positive influence based on this new model. Comprehensive experiments on real social networks are conducted, and results show that previous methods overestimate the overall positive influence, while our model is able to distinguish between negative opinions and positive opinions, and estimate the overall influence more accurately.

On the discovery of critical links and nodes for assessing network vulnerability

Abstract: The assessment of network vulnerability is of great importance in the presence of unexpected disruptive events or adversarial attacks targeting on critical network links and nodes In this paper, we study Critical Link Disruptor (CLD) and Critical Node Disruptor (CND) optimization problems to identify critical links and nodes in a network whose removals maximally destroy the network's functions We provide a comprehensive complexity analysis of CLD and CND on general graphs and show that they still remain NP-complete even on unit disk graphs and power-law graphs Furthermore, the CND problem is shown NP-hard to be approximated within Ω([(n-k)/(ne)] ) on general graphs with n vertices and k critical nodes Despite the intractability of these problems, we propose HILPR, a novel LP-based rounding algorithm, for efficiently solving CLD and CND problems in a timely manner The effectiveness of our solutions is validated on various synthetic and real-world networks

Community Detection in Scale-Free Networks: Approximation Algorithms for Maximizing Modularity

Abstract: Many networks, indifferent of their function and scope, converge to a scale-free architecture in which the degree distribution approximately follows a power law. Meanwhile, many of those scale-free networks are found to be naturally divided into communities of densely connected nodes, known as community structure. Finding this community structure is a fundamental but challenging topic in network science. Since Newman's suggestion of using modularity as a measure to qualify the strength of community structure, many efficient methods that find community structure based on maximizing modularity have been proposed. However, there is a lack of approximation algorithms that provide provable quality bounds for the problem. In this paper, we propose polynomial-time approximation algorithms for the modularity maximization problem together with their theoretical justifications in the context of scale-free networks. We prove that the solutions of the proposed algorithms, even in the worst-case, are optimal up to a constant factor for scale-free networks with either bidirectional or unidirectional links. Even though our focus in this work is not on designing another empirically good algorithms to detect community structure, experiments on real-world networks suggest that the proposed algorithm is competitive with the state-of-the-art modularity maximization algorithm.

Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis

Abstract: The least cost influence (LCI) problem, which asks to identify a minimum number of seed users who can eventually influence a large number of users, has become one of the central research topics recently in online social networks (OSNs). However, existing works mostly focused on a single network while users nowadays often join several OSNs. Thus, it is crucial to investigate the influence in multiplex networks, i.e. the influence is diffused across a set of networks via shared users, in order to obtain the best set of seed users.In this paper, we propose a unified framework to represent and analyze the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem in multiplex OSNs by reducing multiplex networks to a single network via various coupling schemes while preserving the most influence propagation properties. Besides the coupling schemes to represent the diffusion process, the framework also includes the influence relay, a new metric to measure the flow of influence inside and between networks. The experiments on both real and synthesized datasets validate the effectiveness of the coupling schemes as well as provide some interesting insights into the process of influence propagation in multiplex networks.

Influence maximization in multiple online social networks

Abstract: We study the influence maximization problem (IMP) in multiple online social networks (OSNs). In contrast to most of the previous works on influence maximization which only focused on a single network, our work is the first one to evaluate the propagation of influence across multiple networks simultaneously. In this paper, we first propose a general representation for multiple networks using universal ids for the users. We next introduce a powerful coupling scheme which reduces the multiple networks into a single network without changing influencing properties, thereby allowing us to solve the problem in the reduced network. Moreover, the coupling scheme is also an efficient tool to investigate various aspects of the influence propagation on multiple OSNs. The extensive experiments on real-world and synthesized datasets not only confirm the quality of the solution but also reveal interesting insights into the behavior of influence propagation in and across the networks.

An adaptive approximation algorithm for community detection in dynamic scale-free networks

Abstract: We introduce A3CS, an adaptive framework with approximation guarantees for quickly identifying community structure in dynamic networks via maximizing Modularity Q. Our framework explores the advantages of power-law distribution property, is scalable for very large networks, and more excitingly, possesses approximation factors to ensure the quality of its detected community structure. To the best of our knowledge, this is the first framework that achieves approximation guarantees for the NP-hard modularity maximization problem, especially on dynamic networks. To certify our approach, we conduct extensive experiments in comparison with other adaptive methods on both synthesized networks with known community structures and real-world traces including ArXiv e-print citation and Facebook social networks. Excellent empirical results not only confirm our theoretical results but also promise the practical applicability of A3CS in a wide range of dynamic networks.

On Centralized and Localized Approximation Algorithms for Interference-Aware Broadcast Scheduling

Abstract: Broadcast scheduling in multihop Wireless Sensor Networks (WSNs) is an effective mechanism to perform interference-aware broadcasting. Existing works provide centralized solutions, which cannot be implemented locally. Additionally, they consider very elementary network and interference models, in which, either all sensor nodes have the same transmission range or their transmission ranges are equal to their interference ranges that are not very practical. Furthermore, they entirely ignore the existence of WSNs in 3D. In this paper, we study the broadcast scheduling in 2D and 3D WSNs. We consider that sensor nodes may have different transmission ranges and their interference ranges are α times of their transmission ranges (where α >; 1). We devise efficient coloring methods for coloring a hexagonal tiling in 2D plane and a truncated octahedron tiling in 3D space, based on which we propose O(1)-centralized approximation algorithms and O(1)-localized approximation algorithms for the broadcast scheduling problem in 2D and 3D WSNs, respectively. Our O(1)-centralized approximation algorithms for 3D WSNs and O(1)-localized approximation algorithms for 2D and 3D WSNs are the first approximation algorithms for the corresponding problems. Finally, we present an efficient greedy heuristic to study the effect of various priority metrics for greedily scheduling multiple interfering transmissions. Theoretical analysis and experimental results are provided to evaluate the performance of our algorithms.

Adaptive approximation algorithms for hole healing in hybrid wireless sensor networks

Abstract: Region coverage and network connectivity are among the most important problems for the quality of service in wireless sensor networks. Unfortunately, due to the sensor failures and hostile environments, such as active volcanic regions or battle fields, the emergence of coverage holes and disconnections among sensors is unavoidable. One way to handle this problem is to deploy mobile sensors in the network, which is called hybrid sensor networks, so that these mobile sensors can be relocated to heal the holes or maintain the network connectivity. However, because of the low-power of mobile sensors, it is extremely challenging to design a fast and effective movement schedule for mobile sensors to (1) maintain both the region coverage and network connectivity at any time, and (2) minimize the moving energy consumption. In this paper, we develop an adaptive algorithm, AHCH algorithm, to adaptively heal the holes with the guarantee of network connectivity without recomputing from scratch. By comparing AHCH algorithm with the optimal solution at each time-slot, we show its expected adaptive approximation ratio as O(log |M|) with |M| mobile sensors in some special cases. In more general cases, we extend our AHCH algorithm to InAHCH and GenAHCH algorithms, handling insufficient mobile sensors as well as disconnected regions, along with the proof of their corresponding theoretical adaptive approximation ratios. The experimental evaluation shows the effectiveness of our proposed algorithms with respect to both low energy consumption and hole healing latency.

Assessing network vulnerability in a community structure point of view

Abstract: We introduce Community structure Vulnerability Assessment (CVA) problem to assess the network vulnerability under a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable nodes whose removals maximally transform the current network community structure to a different one. As the first attempt, we suggest an approximation algorithm for the special case k = 1, and propose multiple greedy algorithms for CVA problem. To certify the effectiveness of suggested approaches, we test them on not only synthesized networks with known community structures but also on real-world social traces.

Efficient Multi-Link Failure Localization Schemes in All-Optical Networks

Abstract: Link failure localization has been an important and challenging problem for all-optical networks. The most general monitoring structure, called m-trail, is a light-path into which optical signals are launched and monitored. How to minimize the number of required m-trails is critical to the expense of this technique. Existing solutions are limited to localizing single link failure or handling only small networks. Moreover, some practical constraints, like lacking of knowledge of the failure quantity, are ignored. To overcome these limitations is prospective but quite challenging. To this end, we provide novel theoretical solution frameworks toward the multi-failure localization problem. On one hand, for small dense networks, we provide a tree-decomposition based algorithm; on the other hand, a random walk based localized algorithm for large scale sparse networks is proposed. In addition, we further adapt these two algorithms to cope with three practical constraints. Theoretical analysis and simulation results are included to prove the correctness and efficiency of the proposed schemes.

A Unified Approach for Domination Problems on Different Network Topologies

Abstract: We provide tight hardness results and approxiamation algor ithms for many existing domination problems. We start with the positive influence dominating set(PIDS) problem, originated from the context of influence pro pagation in social networks. The PIDS problem seeks for a minimal set of nodes P such that all other nodes in the network have at least a fraction ρ > 0 of their neighbors inP; in the total version (T-PIDS), nodes in P are required to have a fraction ρ of neighbors insideP; and in theconnectedversion (C-PIDS) the dominating set have to induce a connected subgraph. Then, we unify a large variations of do minating set problem under a single generalized dominating set problem. We show a tight hardness results 1 2(1− o(1)) lnn inapproximability and a ln∆ + O(1) approximation algorithms for our generalized dominating s et problem wheren is the newtork size and ∆ is the maximum degree. The results apply directly to PIDS, k-tuple dominating set, m-connectedk-dominating set, Fixed Threshold Dominating Set and many existing domination problems plus all connecte d or/and total versions of those problems. As most previous hardness results are NPcompleteness or APXhardness, we effectively close many long-standing approxi mation gaps of domination problems, under the reasonable assumption that NP 6⊂ DTIME(nO(loglogn)). In networks with degrees bounded by a constant B, we show that all problems cannot be approximated within ln B−O(ln lnB), unless P=NP. In dense networks and scale-free networks such as Internet, WWW, social networks , etc. in which degree sequences follows a power-law distribution, we reveal triv ial constant factor approximation algorithms for the class of PIDS-like dominati on problems. Finally, we prove that optimal solution of any domination problems can b e found in linear time for networks with tree topology. Thang N. Dinh University of Florida, Gainesville, FL 32611, US; e-mail: tdinh@cise.ufl.edu Dung T. Nguyen University of Florida, Gainesville, FL 32611, US; e-mail: dtnguyen@cise.ufl.edu My T. Thai University of Florida, Gainesville, FL 32611, US; e-mail: mythai@cise.ufl.edu

Network vulnerability assessment under cascading failures

Abstract: The assessment of network vulnerability is of great importance in the presence of unexpected disruptive events or adversarial attacks, which will lead to a much more devastating consequence especially when failures can be cascaded. In this context, we study the Cascading Vulnerability Node Detection (CVND) optimization problem to identify the most vulnerable nodes in a network whose removals maximally destroy the network's functions after cascading failures, based on the recently proposed effective metric, total pairwise connectivity. Besides its NP-hardness on various graphs, we further show that the CVND problem is NP-hard to be approximated within equation with n vertices and k vulnerable nodes after d-hop failure cascades. Despite the intractability of this problem, we propose TRGA, a novel iterative two-phase algorithm, for efficiently solving the CVND problem in a timely manner. We also formulate the integer linear programming for obtaining the optimal solution. The effectiveness of our solutions is validated on various synthetic and real-world networks.

Hardness and Approximation of Network Vulnerability

Abstract: Assessing network vulnerability is a central research topic to understand networks structures, thus providing an efficient way to protect them from attacks and other disruptive events. Existing vulnerability assessments mainly focus on investigating the inhomogeneous properties of graph elements, node degree, for example; however, these measures and the corresponding heuristic solutions cannot either provide an accurate evaluation over general network topologies