Graph classification based on structural features of significant nodes and spatial convolutional neural networks

Ma, Tinghuai; Wang, Hongmei; Zhang, Lejun; Tian, Yuan; Al-Nabhan, Najla

doi:10.1016/j.neucom.2020.10.060

Cited by 44 publications

(18 citation statements)

References 21 publications

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“…Then, extracted features or nodes extracted 'NE' is transformed to rank 'r NE i ', 'r NE j ' and {\displaystyle X_{i},Y_{i}} 'r s ' using (7) and is given below. The rank 'r s ' is measured based on the pearson correlation coefficient 'ρ', covariance of rank of each extracted nodes 'cov (r NE i , r NE j )', and the standard deviation of each extracted nodes 'σr NE i , σr NE j ' as given below Finally, the influential node is estimated based on the Katz Centrality using (11) and (12). Let us assume the regression coefficient 'β j ' to be between '{0.7to 1.0}' and constant 'α = 0.5', then, the estimator response for node ID 'T 4 ' is given as below.…”

Section: Qualitative Analysismentioning

confidence: 99%

“…The rank 'r s ' is measured based on the pearson correlation coefficient 'ρ', covariance of rank of each extracted nodes 'cov (r NE i , r NE j )', and the standard deviation of each extracted nodes 'σr NE i , σr NE j ' as given below r s = 5000 70.71 * 141.42 = 0.50 Normalization for 'NE j ' is evaluated as given below. Finally, the influential node is estimated based on the Katz Centrality using (11) and (12). Let us assume the regression coefficient 'β j ' to be between '{0.7to 1.0}' and constant 'α = 0.5', then, the estimator response for node ID 'T 4 ' is given as below.…”

Section: Standard Deviation Of 'mentioning

confidence: 99%

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Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

Vk¹

2021

Preprint

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With the epidemic growth of online social networks (OSNs), a large scale research on information dissemination in OSNs has been made an appearance in contemporary years. One of the essential researches is influence maximization (IM). Most research adopts community structure, greedy stage, and centrality measures, to identify the influence node set. However, the time consumed in analyzing the influence node set for edge server placement, service migration and service recommendation is ignored in terms of propagation delay. Considering the above analysis, we concentrate on the issue of time-sensitive influence maximization and maximize the targeted influence spread. To solve the problem, we propose a method called, Trilateral Spearman Katz Centrality-based Least Angle Regression (TSKC-LAR) for influential node tracing in social network is proposed. Besides, two algorithms are used in our work to find the influential node in social network with maximum influence spread and minimal time, namely Trilateral Statistical Node Extraction algorithm and Katz Centrality Least Angle Influence Node Tracing algorithm, respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics, namely, sensitivity, specificity, accuracy, time and influence spread

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Section: Qualitative Analysismentioning

confidence: 99%

Section: Standard Deviation Of 'mentioning

confidence: 99%

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

Vk¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, the focus has shifted towards learning data-driven graph features [12], [14] where taskrelevant features are learned automatically from the graphs in a given dataset. SCNN [15] first design a comprehensive weighting method to measure the significance of vertices in the graph based on multiple indicators to choose the central node sequence,then construct the same size graphs for the central vertices, and extract characteristics of the graph from both local and global aspects. We can then use the calculated graph features for classification.…”

Section: A Graph Classificationmentioning

confidence: 99%

An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

et al. 2021

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Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. Consequently, it is unreasonable to learn graph representation from a set of diverse graphs by a unified graph neural network. Inspired by this, we design an end-to-end Multiplex Graph Neural Network (MxGNN) that learns graph representations with multiple GNNs, and combines them with a learnable method. The main challenge lies with the combination of multiple representation results. Our new findings show that the a priori graph properties do have an effect on the quality of representation learning, which can be used to guide the learning. Our experiments on graph classification with multiple data sets show that the performance of MxGNN is better than the existing graph representation learning methods.INDEX TERMS Graph neural networks, graph classification, graph representation learning.

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“…To solve this issue, a deep learning model was presented in [11] to identify the most influential nodes in a complex network. Graph classification method was applied in [12] for identifying significant nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Min(Y − Y ′ ); Subjectto ∑ |β j | ≤ t p j=1 (12) From the above equation (12), the estimator response is evaluated based on the minimization of total squared error, therefore considering the neighborhood and non-neighborhood set while identifying the influential nodes in social network. The pseudo code representation of Katz Centrality Least Angle Influence Node Tracing is given below.…”

Section: Katz Centrality Least Angle Influence Node Tracingmentioning

confidence: 99%

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

Vk¹,

Balasubramanian²

2021

Wireless Pers Commun

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With the epidemic growth of online social networks (OSNs), a large scale research on information dissemination in OSNs has been made an appearance in contemporary years. One of the essential researches is influence maximization (IM). Most research adopts community structure, greedy stage, and centrality measures, to identify the influence node set. However, the time consumed in analyzing the influence node set for edge server placement, service migration and service recommendation is ignored in terms of propagation delay. Considering the above analysis, we concentrate on the issue of time-sensitive influence maximization and maximize the targeted influence spread. To solve the problem, we propose a method called, Trilateral Spearman Katz Centralitybased Least Angle Regression (TSKC-LAR) for influential node tracing in social network is proposed. Besides, two algorithms are used in our work to find the influential node in social network with maximum influence spread and minimal time, namely Trilateral Statistical Node Extraction algorithm and Katz Centrality Least Angle Influence Node Tracing algorithm, respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics, namely, sensitivity, specificity, accuracy, time and influence spread

show abstract

Graph classification based on structural features of significant nodes and spatial convolutional neural networks

Cited by 44 publications

References 21 publications

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

Trilateral Spearman Katz Centrality Based Least Angle Regression for Influential Node Tracing in Social Network

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