Group Centrality Maximization via Network Design

Medya, Sourav; Silva, Arlei; Singh, Ambuj K.; Basu, Prithwish; Swami, Ananthram

doi:10.1137/1.9781611975321.14

Cited by 30 publications

(11 citation statements)

References 4 publications

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“…An important line of work in graph analysis and mining looked at manipulating the topology to modify different interesting characteristic quantities of the graph, such as shortest paths and related measures [22,50,51,53], various forms of centrality [4,13,19,39,42,52,61], and more [5,6,14,60,63]. Despite the fact that we consider a specific centrality to choose the source of the added edges, these methods cannot be used to solve our task of interest.…”

Section: Related Workmentioning

confidence: 99%

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Haddadan

Menghini

Riondato

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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The topology of the hyperlink graph among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a "polarized" bubble with no access to other opinions. We model readers' behavior as random walks. A node is in a "polarized" bubble if the expected length of a random walk from it to a page of different opinion is large. The structural bias of a graph is the sum of the radii of highly-polarized bubbles. We study the problem of decreasing the structural bias through edge insertions. "Healing" all nodes with high polarized bubble radius is hard to approximate within a logarithmic factor, so we focus on finding the best 𝑘 edges to insert to maximally reduce the structural bias. We present RePBubLik, an algorithm that leverages a variant of the random walk closeness centrality to select the edges to insert. RePBubLik obtains, under mild conditions, a constant-factor approximation. It reduces the structural bias faster than existing edge-recommendation methods, including some designed to reduce the polarization of a graph.

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Section: Related Workmentioning

confidence: 99%

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Haddadan

Menghini

Riondato

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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show abstract

“…These problems aim to optimize network properties or processes under network modifications. Examples include diameter [6], node centrality [5,19] shortest path [7,16,20,21], and influence spread [10,11,18,26] improvement. However, our objective is different from the ones considered by network design studies.…”

Section: Related Workmentioning

confidence: 99%

Network Robustness via Global k-cores

Dey¹,

Maity²,

Medya³

et al. 2020

Preprint

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Network robustness is a measure a network's ability to survive adversarial attacks. But not all parts of a network are equal. Kcores, which are dense subgraphs, are known to capture some of the key properties of many real-life networks. Therefore, previous work has attempted to model network robustness via the stability of its 𝑘-core. However, these approaches account for a single core value and thus fail to encode a global network resilience measure. In this paper, we address this limitation by proposing a novel notion of network resilience that is defined over all cores. In particular, we evaluate the stability of the network under node removals with respect to each node's initial core. Our goal is to compute robustness via a combinatorial problem: find 𝑏 most critical nodes to delete such that the number of nodes that fall from their initial cores is maximized. One of our contributions is showing that it is NPhard to achieve any polynomial factor approximation of the given objective. We also present a fine-grained complexity analysis of this problem under the lens of parameterized complexity theory for several natural parameters. Moreover, we show two applications of our notion of robustness: measuring the evolution of species and characterizing networks arising from different domains.

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“…Huang et al [Huang et al, 2016] investigate the truss decomposition problem in uncertain graphs. Recently, some research focuses on modifying the graph to maximize/minimize the corresponding metric [Bhawalkar et al, 2015;Zhang et al, 2017;Zhu et al, 2018;Medya et al, 2018]. Bhawalkar et al [Bhawalkar et al, 2015] propose the anchored k-core problem, which tries to maximize the k-core by anchoring b nodes, while Zhang et al [Zhang et al, 2017] and Zhu et al [Zhu et al, 2018] investigate the problem of k-core minimization by deleting nodes and edges, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Bhawalkar et al [Bhawalkar et al, 2015] propose the anchored k-core problem, which tries to maximize the k-core by anchoring b nodes, while Zhang et al [Zhang et al, 2017] and Zhu et al [Zhu et al, 2018] investigate the problem of k-core minimization by deleting nodes and edges, respectively. In [Medya et al, 2018], Medya et al try to maximize the node centrality by adding edges to the graph. However, these techniques cannot be extended for our problem.…”

Section: Related Workmentioning

confidence: 99%

Pivotal Relationship Identification: The K-Truss Minimization Problem

Zhu

Zhang

Chen

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.

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Group Centrality Maximization via Network Design

Cited by 30 publications

References 4 publications

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Network Robustness via Global k-cores

Pivotal Relationship Identification: The K-Truss Minimization Problem

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