A General Centrality Framework-Based on Node Navigability

Meo, Pasquale De; Levene, Mark; Messina, Fabrizio; Provetti, Alessandro

doi:10.1109/tkde.2019.2947035

Cited by 25 publications

(3 citation statements)

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“…De Meo et al creatively interpreted node navigability as the property that any node in the graph can be reached through short walks, leading to the concept of potential gain centrality. This centrality measure unifies various walk-based centrality metrics in complex networks [32].…”

Section: Related Workmentioning

confidence: 89%

Identifying critical nodes in complex networks based on neighborhood information

et al. 2023

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The identification of important nodes in complex networks has always been a prominent topic in the field of network science. Nowadays, the emergence of large-scale networks has sparked our research interest in complex network centrality methods that balance accuracy and efficiency. Therefore, this paper proposes a novel centrality method called Spon (Sum of the Proportion Of Neighbors) Centrality, which combines algorithmic efficiency and accuracy. Spon only requires information within the three-hop neighborhood of a node to assess its centrality, thereby exhibiting lower time complexity and suitability for large-scale networks. To evaluate the performance of Spon, we conducted connectivity tests on 16 empirical unweighted networks and compared the monotonicity and algorithmic efficiency of Spon with other methods. Experimental results demonstrate that Spon achieves both accuracy and algorithmic efficiency, outperforming eight other methods, including CycleRatio, Collective Influence, and Social Capital. Additionally, we present a method called W-Spon to extend Spon to weighted networks. Comparative experimental results on 10 empirical weighted networks illustrate that W-Spon also possesses advantages compared to methods such as I-Core and M-Core.

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Section: Related Workmentioning

confidence: 89%

Identifying critical nodes in complex networks based on neighborhood information

et al. 2023

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“…Detecting spammers via the relational structure such as social connections and relationship graphs is a quite popular topic, so how to integrate the relational structure into Spiral? Existing work [42,43,70] discusses various approaches to use social graphs in spam detection, how can we scale the relational structures into a large-scale distributed environment? Furthermore, Spiral agents run in a highly dynamic environment with many unpredictable events such as network traffic interferences, workload interferences, software/hardware failures.…”

Section: Discussionmentioning

confidence: 99%

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Liu

Guan

et al. 2023

IEEE Access

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Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-ofthe-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral's scalability, effective loadbalancing, and efficiency in online spam detection for social networks.

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“…Along with this measure, we consider f (u) = p Walk uu , the diagonal entry of the Walk kernel, as a centrality of node u (this measure is referred to as 'Walk (K ii )') and also f (u) = v =u p uv as centralities of node u ('Walk (K ij )', 'Communicability (K ij )'). The 'Total communicability' [17] is f (u) = v∈V p Comm uv ; as well as the Katz measure, it can be described in terms of "potential gain" [44].…”

Section: Centralities Based On Graph Kernelsmentioning

confidence: 99%

How to choose the most appropriate centrality measure?

Chebotarev¹,

Gubanov²

2020

Preprint

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We propose a new method to select the most appropriate network centrality measure based on the user's opinion on how such a measure should work on a set of simple graphs. The method consists in: (1) forming a set F of candidate measures; (2) generating a sequence of sufficiently simple graphs that distinguish all measures in F on some pairs of nodes; (3) compiling a survey with questions on comparing the centrality of test nodes; (4) completing this survey, which provides a centrality measure consistent with all user responses. The developed algorithms make it possible to implement this approach for any finite set F of measures. This paper presents its realization for a set of 40 centrality measures. The proposed method called culling can be used for rapid analysis or combined with a normative approach by compiling a survey on the subset of measures that satisfy certain normative conditions (axioms). In the present study, the latter was done for the subsets determined by the Self-consistency or Bridge axioms.

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A General Centrality Framework-Based on Node Navigability

Cited by 25 publications

References 50 publications

Identifying critical nodes in complex networks based on neighborhood information

Identifying critical nodes in complex networks based on neighborhood information

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

How to choose the most appropriate centrality measure?

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