Recently, various methods have emerged for predictive modeling across multiple domains to forecast the influence of nodes in complex networks. The existing machine learning based methods predict influential nodes in c...
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Recently, various methods have emerged for predictive modeling across multiple domains to forecast the influence of nodes in complex networks. The existing machine learning based methods predict influential nodes in complex networks by using feature selection techniques for the nodes. However, these methods suffer from limitations in feature engineering. In contrast, Graph Convolutional Networks (GCNs) present a breakthrough in deep learning, offering an effective approach for analysing graph-structured data. This research work proposes the Influential node Prediction based on Graph Convolutional Network (Ip-GCN) method for complex networks, which integrates GCN and Long Short-Term Memory (LSTM) techniques. Initially, the proposed method preprocesses datasets by removing noisy nodes and constructing the adjacency matrix of the graph. The addition of the identity and adjacency matrices accounts for the self-contribution of individual nodes in the graph, and the degree matrix is derived from the updated adjacency matrix. Furthermore, the feature matrix is computed from the adjacency matrix to capture the connections of nodes and the hidden structure of the graph. The scaling of the adjacency matrix is performed using the inverse of the degree matrix, followed by normalization with the feature matrix. The resultant normalized matrix is provided as input to the GCN layer for embeddings. The LSTM layer is then applied to the embedded matrix to predict the influence of nodes. The comparative performance of Ip-GCN is evaluated against existing methods, including deep learning, machine learning and centrality-based methods. The Ip-GCN model significantly enhances performance across all five real-world datasets. It improves the F1 score by 9%, 29%, 14%, 9%, and 18% on Hamsterster Friendship, Human Protein (Vidal), CA-GrQc, CA-HepTh, and CA-CondMat respectively. In addition to that, it enhances accuracy by 5%, 1%, 0.4%, and 4% on Human Protein (Vidal), CA-GrQc, CA-HepTh, and
Background: Wireless Sensor Networks (WSNs) have gained significant attention due to their diverse applications, including border area security, earthquake detection, and fire detection. WSNs utilize compact sensors t...
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The efficient signal processing techniques for spread-spectrum code division multiple access (CDMA) systems necessitate data delivery with higher quality-of-service support, diminished error rates and improved channel...
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It has been observed that Cloud services exhibit suboptimal performance for real-time requests due to increased network delay. Fog computing has emerged to address this issue by deploying Fog nodes at the network'...
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The need for sustainability in modern manufacturing systems is influenced by environmental-driven objectives including lower emissions, minimal utilisation of natural resources, reduced energy usage, etc. In this work...
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The symbols in equations and their descriptions in sections 3 and 4.2 were incorrectly published in original version. The corrected equations and their descriptions are provided below. The most influential node o...
The symbols in equations and their descriptions in sections 3 and 4.2 were incorrectly published in original version. The corrected equations and their descriptions are provided below. The most influential node of network is computed from the adjacency matrix. This has impact on the opinions of the other community members. In this step, an adjacency matrix A is obtained from the network according to the connections between each pair of nodes. The adjacency matrix A is of size n×n, where n is the number of nodes. The nodes can be denoted as v1,v2⋯⋯vn. The element of the matrix, aij, represents the connection between nodes vi and vj. An edge set of the nodes is denoted by E. The adjacency matrix A(n×n) is computed with respect to Eq. (1). (Formula presented.) The transition probability matrix P(n×n) is computed from the adjacency matrix A(n×n). The transition probability pi,j is set to 1 if node vi is only connected to node vj. It means that node vj is significant for node vi. It determines the weight and importance of each node in a graph. (Formula presented.) The row matrix, 1×n is considered and each element weight is set to 1 and this weight matrix is multiplied with transition probability matrix to obtain limiting matrix W*. This limiting matrix reflects the importance of nodes according to their weight. The node having the highest weight is the most Influential node in the network. It is represented by i_leader. (Formula presented.) It can be written as (Formula presented.) (Formula presented.) The limiting weight matrix W* represents the long-term behavior of the weights of the nodes in the network. It is a fixed point of the product of W and Pm as m approaches infinity. (Figure presented.) Most Influential Node Identification The Algorithm 1 determines the most influential node vi (i_leader) from the set of nodes (v1,v2⋯…vn) of the network. The Euclidean distance [43] of node vi (i_leader) is computed for all the nodes. This Euclidean distance score represents
The generalised model for multipath signal estimation is developed in this work with distinct optimal gain and receives diversity operation deployed in the wireless system. The presented modelling technique determines...
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Service-oriented architecture (SOA) may effectively be implemented in distributed software development like cloud computing, internet of things, etc. Expectations from these systems, in terms of their reliability and ...
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In this paper, a reliable and efficient data routing scheme is developed that employs the conventional AODV protocol based on dynamic genetic algorithm. It is aimed at effectively allocating the scarce radio resources...
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作者：
Rawat, AkhileshKumar, RajeevLab
School of Computer & ampSystems Sciences Jawaharlal Nehru University New Delhi110 067 India
In this work, we augment the 3D U-Net architecture with state-of-the-art (SOTA) generalization techniques using proper parameterization. We empirically assess the normalization and regularization technique(s) with a s...
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