Transitivity Demolition and the Fall of Social Networks

Nguyen, Hung T.; Nguyen, Nam; Vu, Tam; Hoang, Huan X.; Dinh, Thang N.

doi:10.1109/access.2017.2672666

Cited by 10 publications

(4 citation statements)

References 51 publications

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“…Looking at recent studies, Nguyen et al [48] investigate the vulnerability of social networks using network theory. Critical nodes and edges are identified by computing the number of connected triples (or triangles) that are broken, when a failure (either random or intentional) occurs, causing changes in the network's organization and leading to the unpredictable dissolving of the network.…”

Section: Operation Research and Managementmentioning

confidence: 99%

Resilience of Complex Systems: State of the Art and Directions for Future Research

2018

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This paper reviews the state of the art on the resilience of complex systems by embracing different research areas and using bibliometric tools. The aim is to identify the main intellectual communities and leading scholars and to synthesize key knowledge of each research area. We also carry out a comparison across the research areas, aimed at analyzing how resilience is approached in any field, how the topic evolved starting from the ecological field of study, and the level of cross-fertilization among domains. Our analysis shows that resilience of complex systems is a multidisciplinary concept, which is particularly important in the fields of environmental science, ecology, and engineering. Areas of recent and increasing interest are also operation research, management science, business, and computer science. Except for environmental science and ecology, research is fragmented and carried out by isolated research groups. Integration is not only limited inside each field but also between research areas. In particular, we trace the citation links between different research areas and find a very limited number, revealing a scarce cross-fertilization among domains. We conclude by providing some directions for future research.

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Section: Operation Research and Managementmentioning

confidence: 99%

Resilience of Complex Systems: State of the Art and Directions for Future Research

2018

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“…These approaches are restricted to the scope of online social networks and do not cater to general network structures. Another work by Nguyen et al [24] explored the number of connected triplets in a network as they capture the strong connection of communities in social networks.…”

Section: Related Workmentioning

confidence: 99%

Understanding Vulnerability of Communities in Complex Networks

Parimi¹,

Pal²,

Ruj³

et al. 2019

Preprint

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In this paper, we study the crucial elements of complex networks, namely nodes, and edges and their properties such as their community structure, which play an important role in dictating the robustness of the network towards structural perturbations. Specifically, we want to identify all vital nodes, which when removed would lead to a large change in the underlying community structure of the network. This problem is extremely important because the community structure of a network allows deep underlying insights into how function of a network and its topology affect each other. Moreover, it even provides a way to condense large graphs into smaller graphs where each community acts as a meta node and hence aids in easier network analysis. If this community structure was to be compromised by either accidental or intentional perturbations to the network, that would make such analysis difficult. Since the problem of identifying such vital nodes is computationally intractable, we propose some heuristics that allow us to find solutions close to the optimal solution. To certify the effectiveness of our approach, we first test these heuristics on small networks, and then move to larger networks to show that we achieve similar results. The results reveal that the proposed approaches are effective to analyze the vulnerability of communities in graphs irrespective of their size and scale. From the application point of view, we show that the proposed algorithm is scalable and can be applied to the information diffusion task to curtail the spread of active nodes which was observed empirically. Additionally, we show the performance of our algorithm through an extrinsic evaluation -we employ two tasks, like prediction and information diffusion, and show that the effect of our algorithm on these tasks is higher than the other baselines.

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“…However, when applied to distribution networks with high penetration of distributed power sources, its robustness is low. Nguyen et al [19] used a time recurrent graph neural network (TR-GNN) to extract spatiotemporal features from voltage measurement unit data, performing fault-type classification, line selection, and fault localization in distribution networks. However, the model's input features are relatively singular, only extracting spatiotemporal features from voltage measurement unit data, without comparing the results of using current measurement data as fault detection features.…”

Section: Introductionmentioning

confidence: 99%

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

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When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

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Transitivity Demolition and the Fall of Social Networks

Cited by 10 publications

References 51 publications

Resilience of Complex Systems: State of the Art and Directions for Future Research

Resilience of Complex Systems: State of the Art and Directions for Future Research

Understanding Vulnerability of Communities in Complex Networks

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

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