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136
Streaming Algorithms for kcore Decomposition
"... A kcore of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. kcore decomposition is often used in largescale network analysis, such as community detection, protein function prediction, visualization, and solving NPHard problems on ..."
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Cited by 8 (2 self)
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on real networks efficiently, like maximal clique finding. In many realworld applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for streaming graph data. In this paper, we propose the first incremental kcore decomposition algorithms
Preventing Unraveling in Social Networks: The Anchored kCore Problem
"... We consider a model of user engagement in social networks, where each player incurs a cost to remain engaged but derives a benefit proportional to the number of engaged neighbors. The natural equilibrium of this model corresponds to the kcore of the social network — the maximal induced subgraph w ..."
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Cited by 6 (0 self)
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with minimum degree at least k. We study the problem of “anchoring” a small number of vertices to maximize the size of the corresponding anchored kcore — the maximal induced subgraph in which every nonanchored vertex has degree at least k. This problem corresponds to preventing “unraveling” — a cascade
Routing for Network Capacity Maximization in Energyconstrained Adhoc Networks
, 2003
"... We present a new algorithm for routing of messages in adhoc networks where the nodes are energyconstrained. The routing objective is to maximize the total number of messages that can be successfully sent over the network without knowing any information regarding future message arrivals or message ..."
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Cited by 82 (0 self)
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necessary only to prove the competitive ratio result), and that it also performs better than previously proposed algorithms for other suggested metrics such as network lifetime maximization. Our algorithm uses a single shortest path computation, and is amenable to efficient implementation. We also evaluate
Finding Maximal Cliques in Massive Networks by H*graph
"... Maximal clique enumeration (MCE) is a fundamental problem in graph theory and has important applications in many areas such as social network analysis and bioinformatics. The problem is extensively studied; however, the best existing algorithms require memory space linear in the size of the input gr ..."
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Cited by 26 (14 self)
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Maximal clique enumeration (MCE) is a fundamental problem in graph theory and has important applications in many areas such as social network analysis and bioinformatics. The problem is extensively studied; however, the best existing algorithms require memory space linear in the size of the input
An efficient and complete approach for throughputmaximal sdf allocation and scheduling on multicore platforms
 in Design, Automation Test in Europe Conference Exhibition (DATE
, 2010
"... Abstract—Our work focuses on allocating and scheduling a synchronous dataflow (SDF) graph onto a multicore platform subject to a minimum throughput requirement. This problem has traditionally be tackled by incomplete approaches based on problem decomposition and local search, which could not guara ..."
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Cited by 17 (2 self)
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Abstract—Our work focuses on allocating and scheduling a synchronous dataflow (SDF) graph onto a multicore platform subject to a minimum throughput requirement. This problem has traditionally be tackled by incomplete approaches based on problem decomposition and local search, which could
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate
"... Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that ..."
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Cited by 28 (7 self)
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graphs and show a tight bound of Θ ( √ n/k) on the qsratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to compute influence in tree
A Hybrid Genetic Algorithm / Variable Neighborhood Search Approach to Maximizing Residual Bandwidth of Links for Route Planning
"... Abstract. This paper proposes a novel approach to performing residual bandwidth optimization with QoS guarantees in multiclass networks. The approach combines the use of a new highly scalable hybrid GAVNS algorithm (Genetic Algorithm with Variable Neighborhood Search) with the efficient and accur ..."
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Abstract. This paper proposes a novel approach to performing residual bandwidth optimization with QoS guarantees in multiclass networks. The approach combines the use of a new highly scalable hybrid GAVNS algorithm (Genetic Algorithm with Variable Neighborhood Search) with the efficient
Maximal independent sets in multichannel radio networks.
, 2013
"... ABSTRACT We present new upper bounds for fundamental problems in multichannel wireless networks. These bounds address the benefits of dynamic spectrum access, i.e., to what extent multiple communication channels can be used to improve performance. In more detail, we study a multichannel generalizat ..."
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Cited by 1 (0 self)
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generalization of the standard graphbased wireless model without collision detection, and assume the network topology satisfies polynomially bounded independence. Our core technical result is an algorithm that constructs a maximal independent set (MIS) in O ( log 2 n F ) +Õ(log n) rounds, in networks of size n
Routing for Network Capacity Maximization in Energyconstrained Adhoc Networks
"... Abstract — We present a new algorithm for routing of messages in adhoc networks where the nodes are energyconstrained. The routing objective is to maximize the total number of messages that can be successfully sent over the network without knowing any information regarding future message arrivals ..."
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control being necessary only to prove the competitive ratio result), and that it also performs better than previously proposed algorithms for other suggested metrics such as network lifetime maximization. Our algorithm uses a single shortest path computation, and is amenable to efficient implementation
Efficient Algorithms for Some Variants of the Farthest String Problem
"... The farthest string problem (FARTHEST STRING) is one of the core problems in the field of consensus word analysis and several biological problems such as discovering potential drugs, universal primers, or unbiased consensus sequences. Given k strings of the same length L and a nonnegative integer d, ..."
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Cited by 1 (0 self)
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between s and all the given strings are greater than d. We give an O((Σ(Ld)) (Ld))time algorithm, where  Σ  is the alphabet size. The other variant is to find a string s such that the sum of the hamming distances between s and all the given strings are maximized. We solve this problem in O ( k
Results 1  10
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