Core-Intermediate-Peripheral Index: Factor Analysis of Neighborhood and Shortest Paths-based Centrality Metrics

• URL: http://arxiv.org/abs/2310.06358v1
• Date: Tue, 10 Oct 2023 06:52:20 GMT
• Title: Core-Intermediate-Peripheral Index: Factor Analysis of Neighborhood and Shortest Paths-based Centrality Metrics
• Authors: Natarajan Meghanathan
• Abstract summary: We propose a novel measure called the Core- Intermediate-Peripheral (CIP) Index to capture the extent with which a node could play the role of a core node. We test our approach on a diverse suite of 12 complex real-world networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We perform factor analysis on the raw data of the four major neighborhood and shortest paths-based centrality metrics (Degree, Eigenvector, Betweeenness and Closeness) and propose a novel quantitative measure called the Core-Intermediate-Peripheral (CIP) Index to capture the extent with which a node could play the role of a core node (nodes at the center of a network with larger values for any centrality metric) vis-a-vis a peripheral node (nodes that exist at the periphery of a network with lower values for any centrality metric). We conduct factor analysis (varimax-based rotation of the Eigenvectors) on the transpose matrix of the raw centrality metrics dataset, with the node ids as features, under the hypothesis that there are two factors (core and peripheral) that drive the values incurred by the nodes with respect to the centrality metrics. We test our approach on a diverse suite of 12 complex real-world networks.

Related papers

• Lidar Panoptic Segmentation and Tracking without Bells and Whistles [48.078270195629415]
  We propose a detection-centric network for lidar segmentation and tracking. One of the core components of our network is the object instance detection branch. We evaluate our method on several 3D/4D LPS benchmarks and observe that our model establishes a new state-of-the-art among open-sourced models.
  arXiv  Detail & Related papers  (2023-10-19T04:44:43Z)
• Provable Data Subset Selection For Efficient Neural Network Training [73.34254513162898]
  We introduce the first algorithm to construct coresets for emphRBFNNs, i.e., small weighted subsets that approximate the loss of the input data on any radial basis function network. We then perform empirical evaluations on function approximation and dataset subset selection on popular network architectures and data sets.
  arXiv  Detail & Related papers  (2023-03-09T10:08:34Z)
• Bandwidth-efficient distributed neural network architectures with application to body sensor networks [73.02174868813475]
  This paper describes a conceptual design methodology to design distributed neural network architectures. We show that the proposed framework enables up to a factor 20 in bandwidth reduction with minimal loss. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.
  arXiv  Detail & Related papers  (2022-10-14T12:35:32Z)
• CenterNet++ for Object Detection [174.59360147041673]
  Bottom-up approaches are as competitive as the top-down and enjoy higher recall. Our approach, named CenterNet, detects each object as a triplet keypoints (top-left and bottom-right corners and the center keypoint) On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieves APs of 53.7% and 57.1%, respectively.
  arXiv  Detail & Related papers  (2022-04-18T16:45:53Z)
• A Modular Framework for Centrality and Clustering in Complex Networks [0.6423239719448168]
  In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information-flow based model is adopted for clustering, which itself builds upon an information theoretic measure for computing centrality. Our clustering naturally inherits the flexibility to accommodate edge directionality, as well as different interpretations and interplay between edge weights and node degrees.
  arXiv  Detail & Related papers  (2021-11-23T03:01:29Z)
• Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis [0.0]
  Bi-face (BF) is a new bipartite centrality measurement for identifying important nodes in two-mode networks. Unlike off-the shelf centrality indices, it quantifies how a node has a cohesive-substructure influence on its neighbour nodes via bicliques. Our experiments on several real-world and synthetic networks show the efficiency of BF over existing prominent bipartite centrality measures.
  arXiv  Detail & Related papers  (2021-09-07T23:57:05Z)
• Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical [85.0376670244522]
  We investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. We develop a Star-shaped Reasoning Graph Network (S-RGN) to exploit more powerful and flexible capacity. We also develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity.
  arXiv  Detail & Related papers  (2021-09-06T03:16:56Z)
• A QUBO formulation for top-$\tau$ eigencentrality nodes [0.0]
  We lay the foundations for solving the eigencentrality problem of ranking the importance of the nodes of a network with scores from the eigenvector of the network. The problem is reformulated as a quadratic unconstrained binary optimization (QUBO) that can be solved on both quantum architectures. The results focus on correctly identifying a given number of the most important nodes in numerous networks given by the sparse vector solution of our QUBO formulation of the problem of identifying the top-$tau$ highest eigencentrality nodes in a network on both the D-Wave and IBM quantum computers.
  arXiv  Detail & Related papers  (2021-05-01T05:35:44Z)
• Spectral Embedding of Graph Networks [76.27138343125985]
  We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure and neighborhood proximity in a single representation.
  arXiv  Detail & Related papers  (2020-09-30T04:59:10Z)
• Top influencers can be identified universally by combining classical centralities [0.6853165736531939]
  No single centrality has consistently good ranking power. Certain pairs of centralities cooperate particularly well in statistically drawing the boundary between the top spreaders and the rest. nodes selected as superspreaders will usually jointly maximise the values of both centralities.
  arXiv  Detail & Related papers  (2020-06-13T15:12:28Z)
• Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring [3.0724051098062097]
  We prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. We propose Centroid Based Tailoring as an alternative to the softmax function in the last layer of a neural network.
  arXiv  Detail & Related papers  (2020-01-07T11:47:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.