Understanding Stability of Noisy Networks through Centrality Measures and Local Connections

Ufimtsev, Vladimir; Sarkar, Soumya; Mukherjee, Animesh; Bhowmick, Sanjukta

doi:10.1145/2983323.2983692

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Most graph anonymization algorithms try to minimize the number of changes. Our studies in (Ufimtsev et al, 2016;Cheon et al, 2018) have shown that the changes to the higher cores disrupt the centrality values more than changes to the periphery.…”

Section: Applications For the Proposed Methodsmentioning

confidence: 62%

Investing Data with Untrusted Parties using HE

Dockendorf

Dantu

Morozov

et al. 2021

Proceedings of the 18th International Conference on Security and Cryptography

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Data such as an individual's income, favorite sports team, typical commute route, vehicle maintenance history, medical records, etc. are typically not useful for making large-scale decisions such as where to build a new hospital, identifying which roads are in need of upkeep, and the like. However, aggregates of of these data across hundreds of individuals are useful to governments and to companies. Data cooperatives/unions offer a place for individuals to store their data and a service of data aggregation and interpretation to governments, non-profit organizations, and businesses while maintaining individuals' anonymity. We propose the use of anonymization techniques coupled with graph algorithms over homomorphically encrypted (HE) graphs as a basis of analysis for this accumulated data. We believe this approach ensures individuals' privacy and anonymity while preserving the usefulness of the plaintext data.

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Section: Applications For the Proposed Methodsmentioning

confidence: 62%

Investing Data with Untrusted Parties using HE

Dockendorf

Dantu

Morozov

et al. 2021

Proceedings of the 18th International Conference on Security and Cryptography

Self Cite

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“…It underscores the importance of considering the reliability of network connections in assessing overall network stability. Ufmtsev et al [27] reveal that the impact of structural noise on centrality ranks is examined. Tis study complements our approach by highlighting how minor structural changes can signifcantly infuence the stability and centrality of nodes, thereby afecting the network's resilience to disruptions.…”

Section: Assessment Of Network Stabilitymentioning

confidence: 99%

“…Tis section evaluates four types of centrality in the directed, weighted network: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. Tis is echoed in the work of Ufmtsev et al [27], who emphasize the impact of centrality measures on understanding the stability of networks, especially under conditions of noise and disturbance. Tis section evaluates four types of centrality in the directed, weighted network: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality.…”

Section: Centrality Measuresmentioning

confidence: 99%

Research on the Statistical Properties and Stability of Complex Interindustrial Networks

Cheng

2024

Complexity

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This study consolidates input-output data from 42 sectors across 31 provinces and regions in China into a unified dataset for 42 industrial sectors within eight major economic zones. Leveraging the maximum entropy method, we identify significant interindustrial relationships, subsequently forming a directed, weighted, complex network of these ties. Building upon this intricate network, we analyze its foundational statistical attributes. The stability of the network’s structure is further assessed through simulations of varied network attacks. Our findings demonstrate that the maximum entropy method is adept at extracting notable relationships between industrial sectors, facilitating the creation of a cogent complex interindustrial network. Although this established network exhibits high stability, it calls for targeted policy interventions and risk management, especially for industries with pronounced degree centrality and betweenness centrality. These pivotal industry nodes play a decisive role in the overall stability of the network. The insights derived from our examination of complex interindustrial networks illuminate the structure and function of industrial networks, bearing profound implications for policymaking and propelling sustainable, balanced economic progress.

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“…As a first step, we note that if most of the shortest paths pass through the innermost core, then the high centrality vertices would also be part of the innermost core. Moreover, if these vertices are tightly connected to each other, they can enhance each others' centrality values [37]. Thus, a dense innermost core through which most of the shortest paths pass provides us a smaller subset in which to search for high centrality vertices.…”

Section: Our Hypothesismentioning

confidence: 99%

Using core-periphery structure to predict high centrality nodes in time-varying networks

Sarkar

Sikdar

Mukherjee

et al. 2018

Data Min Knowl Disc

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Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves.In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree.To measure the effectiveness of our framework, we perform prediction task on a large set of diverse time-varying networks. We obtain F 1-scores as high as 0.81 and 0.72 in predicting the top m closeness and betweenness centrality vertices respectively for real networks where the highly central vertices Soumya Sarkar 2 Sarkar et al mostly reside in the innermost core. For synthetic networks that conform to this property we achieve F 1-scores of 0.94 and 0.92 for closeness and betweenness respectively. We validate our results by showing that the practical effects of our predicted vertices match the effects of the actual high centrality vertices. Finally, we also provide a formal sketch demonstrating why our method works.

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Understanding Stability of Noisy Networks through Centrality Measures and Local Connections

Cited by 5 publications

References 9 publications

Investing Data with Untrusted Parties using HE

Investing Data with Untrusted Parties using HE

Research on the Statistical Properties and Stability of Complex Interindustrial Networks

Using core-periphery structure to predict high centrality nodes in time-varying networks

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