From Complex Network to Skeleton: $$ \varvec{m}_{\varvec{j}} $$-Modified Topology Potential for Node Importance Identification

Yuan, Huang; Malang, Kanokwan; Lv, Yuanyuan; Phaphuangwittayakul, Aniwat

doi:10.1007/978-3-030-05090-0_35

Cited by 3 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e parameter σ is a factor used to control the influence region of each node. m j represents the mass of the node, which is redefined by k-shell centrality, and m j � KS v [8]. Accordingly, the influence value of the nodes is induced by a different value of m j in an improved TP algorithm, namely, TPKS.…”

Section: Influential Nodes By An Improved Topology Potentialmentioning

confidence: 99%

“…In this paper, the advantages of analyzing the global terrorism network are under the discrimination of the network's core structure. Generally speaking, instead of investigating the significant information from the entire network, we pay attention to the skeleton network [8]. e skeleton network relies on the concept of minimizing the network's scale and complexity [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Influential Nodes By An Improved Topology Potentialmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Motif-h: a novel functional backbone extraction for directed networks

Bai

Fan

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Dense networks are very pervasive in social analytics, biometrics, communication, architecture, etc. Analyzing and visualizing such large-scale networks are significant challenges, which are generally met by reducing the redundancy on the level of nodes or edges. Motifs, patterns of the higher order organization compared with nodes and edges, are recently found to be the novel fundamental unit structures of complex networks. In this work, we proposed a novel motif h-backbone (Motif-h) method to extract functional cores of directed networks based on both motif strength and h-bridge. Compared with the state-of-the-art method Motif-DF and Entropy, our method solves two main issues which are often found in existing methods: the Motif-h reconsiders weak ties into our candidate set, and those weak ties often have critical functions of bridges in networks; moreover, our method provides a trade-off between the motif size and the edge strength, which quantifies the core edges accordingly. In the simulations, we compare our method with Motif-DF in four real-world networks and found that Motif-h can streamline the extraction of crucial structures compared with the others with limited edges.

show abstract

Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Malang

Wang

et al. 2020

International Journal of Data Warehousing and Mining

Self Cite

View full text Add to dashboard Cite

Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.

show abstract

From Complex Network to Skeleton: $$ \varvec{m}_{\varvec{j}} $$-Modified Topology Potential for Node Importance Identification

Cited by 3 publications

References 23 publications

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

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

Motif-h: a novel functional backbone extraction for directed networks

Skeleton Network Extraction and Analysis on Bicycle Sharing Networks

Contact Info

Product

Resources

About