Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction

Malang, Kanokwan; Wang, Shuliang; Phaphuangwittayakul, Aniwat; Lv, Yuanyuan; Yuan, Huang; Zhang, Xiuzhen

doi:10.1016/j.physa.2019.123769

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…Nevertheless, the modelling of criminal behaviours involves the analysis of the spatio-temporal patterns of the commission of the crimes [23,27]. This fact is the reason that network theory is useful for the drawing inferences on antilegislative actions and policies [18][19][20][21]23,[28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Entropy has also recently been used to predict criminal behaviours [48][49][50]. Prediction involves the study of temporal networks [51,52], of particular interest in criminal investigations, because the role and/or effectiveness of the nodes usually changes in time [28,29,33,34,47,53]. High entropy of some local network properly indicates that many nodes share this property in more or less the same degree.…”

Section: Introductionmentioning

confidence: 99%

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Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

Spyropoulos

Bratsas

Makris

et al. 2021

Entropy

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In recent years, law enforcement authorities have increasingly used mathematical tools to support criminal investigations, such as those related to terrorism. In this work, two relevant questions are discussed: “How can the different roles of members of a terrorist organization be recognized?” and “are there early signs of impending terrorist acts?” These questions are addressed using the tools of entropy and network theory, more specifically centralities (degree, betweenness, clustering) and their entropies. These tools were applied to data (physical contacts) of four real terrorist networks from different countries. The different roles of the members are clearly recognized from the values of the selected centralities. An early sign of impending terrorist acts is the evolutionary pattern of the values of the entropies of the selected centralities. These results have been confirmed in all four terrorist networks. The conclusion is expected to be useful to law enforcement authorities to identify the roles of the members of terrorist organizations as the members with high centrality and to anticipate when a terrorist attack is imminent, by observing the evolution of the entropies of the centralities.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

Spyropoulos

Bratsas

Makris

et al. 2021

Entropy

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“…e source node which is terrorist can pass its impact to the target through the reachable path in network topology structure [36]. As previously tested and confirmed by [37], TPKS provides a great potential application in the context of skeleton network extraction. e reason is straightforward: TPKS is more likely to create a skeleton network from a small set of highly influential nodes.…”

Section: Influential Nodes By An Improved Topology Potentialmentioning

confidence: 75%

Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential

et al. 2020

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Skeleton network extraction is a crucial context in studying the core structure and essential information on complex networks. The objective of this paper is to introduce the novel network extraction method, namely, TPKS-skeleton, for investigating the global terrorism network. Our method aims to reduce the network’s size while preserving key topology and spatial features. A TPKS-skeleton comprises three steps: node evaluation, similarity-based clustering, and skeleton network reconstruction. The importance of skeleton nodes is quantified by the improved topology potential algorithm. Similarity-based clustering is then integrated to allow detecting high incident concentrations and allocating the important nodes according to the event features and spatial distribution. Finally, the skeleton network can be reconstructed by aggregating high-influential nodes from each cluster and their simplified edges. To verify the efficiency of the proposed method, we carry out three classes of a network assessment framework: node-equivalence assessment, network-equivalence assessment, and spatial information assessment. For each class, various assessment indexes were performed using the original network as a benchmark. The results verify that our proposed TPKS-skeleton outperforms other competitive methods in particular node-equivalence by Spearman rank correlation and high network structural-equivalence defined by quadratic assignment procedure. In the spatial perspective, the TPKS-skeleton network preserves reasonably all kinds of spatial information. Our study paves the way to extract the optimal skeleton of the global terrorism network, which might be beneficial for counterterrorism and network analysis in wider areas.

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“…For nodes, a range of indices such as degree (Carmi et al, 2007;Kitsak et al, 2010;Siganos, Tauro, & Faloutsos, 2006), local centrality (Chen et al, 2012) and Leader Rank (Li et al, 2014;Lv et al, 2011) have been applied to identify influential nodes. Further, backbone network can be obtained by reserving the smallest possible subset of highly influential nodes and their relevant links (Malang et al, 2020). As for edges, link weights (Zhang & Zhu, 2013;Zhang et al, 2018;Zhao et al, 2014) and network motifs (Cao, Ding, & Shi, 2019) were used to extract functional backbones while edge betweenness (Kim, Noh, & Jeong, 2004) and the shortest path (Grady, Thiemann, & Brockmann, 2011;Zhang et al, 2014) were used to preserve more structural features.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Xing

Han

2021

Journal of Data and Information Science

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Purpose With the availability and utilization of Inter-Country Input-Output (ICIO) tables, it is possible to construct quantitative indices to assess its impact on the Global Value Chain (GVC). For the sake of visualization, ICIO networks with tremendous low- weight edges are too dense to show the substantial structure. These redundant edges, inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis (SNA). In this case, we need a method to filter such edges and obtain a sparser network with only the meaningful connections. Design/methodology/approach In this paper, we propose two parameterless pruning algorithms from the global and local perspectives respectively, then the performance of them is examined using the ICIO table from different databases. Findings The Searching Paths (SP) method extracts the strongest association paths from the global perspective, while Filtering Edges (FE) method captures the key links according to the local weight ratio. The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks. Research limitations There are still two limitations in this research. One is that the computational complexity may increase rapidly while processing the large-scale networks, so the proposed method should be further improved. The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology. Practical implications The network pruning methods we proposed will promote the analysis of the ICIO network, in terms of community detection, link prediction, and spatial econometrics, etc. Also, they can be applied to many other complex networks with similar characteristics. Originality/value This paper improves the existing research from two aspects, namely, considering the heterogeneity of weights and avoiding the interference of parameters. Therefore, it provides a new idea for the research of network backbone extraction.

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Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction

Cited by 14 publications

References 31 publications

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

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