Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach

Rezaei, Ahmad Asgharian; Munoz, Justin; Jalili, Mahdi; Khayyam, Hamid

doi:10.48550/arxiv.2202.06229

Cited by 5 publications

(6 citation statements)

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“…Let us consider the complex network with 18 edges and 14 nodes, as shown in Figure 2. [30][31][32] In the K-shell decomposition of the above-mentioned network, degree one is assigned for Nodes I, II, IX, XIV, XII, and XIII and are removed from the network during the first iteration, which is marked in blue color. Here, all the nodes with degree one are assigned with the K-index I k ¼ 1 and again recursively check for new nodes with degree one.…”

Section: K-shell Decompositionmentioning

confidence: 99%

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

Seshu Chakravarthy,

Selvaraj

2024

Int J Communication

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SummaryThe influential node in the network is the node that has a higher impact on network functioning compared to the other nodes. The influential node detection in the complex network is crucial for rumor containment, virus spreading, viral marketing, and so forth. The researchers designed several influential node detection methods; still, detecting community and influential node selection with minimal computational complexity by considering the relationship between the nodes is challenging. Hence, an optimal community detection along with the hybrid K‐shell decomposition method is introduced in this research. Initially, the optimal community from the complex network is identified to reduce the computation burden. For this, the Improved Dingo (IDingo) algorithm is introduced by hybridizing the hunting behavior of Dingo and the rough encircling behavior of Harris Hawk. After detecting the optimal community, the influential node identification is devised using the proposed hybrid K‐shell decomposition methods. The potential edge weights are considered while ranking the nodes. The performance of a proposed method is analyzed using six various datasets and accomplished the maximal cluster coefficient of 0.56578, 0.25674, 0.24022, 0.5968, 0.23419, and 0.10196 for Karate, Dolphins, C‐Elegance, Facebook, Gowalla, and Power Grid Dataset.

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Section: K-shell Decompositionmentioning

confidence: 99%

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

Seshu Chakravarthy,

Selvaraj

2024

Int J Communication

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“…Rezaei et al [28] Proposed a unique sampling method named cluster sampling, designed to incorporate nodes with varied structural and influential characteristics into the training set. The sampling strategy involves selecting only 0.5% of the complete network, resulting in modest training sets, even for extensive networks.…”

Section: Generalized the Local Centralities ML Algorithms Not Appliedmentioning

confidence: 99%

“…Their method involved employing a classification model for vital node identification and training it on a significant portion of nodes from the original network. Rezaei et al [28] introduced an innovative sampling technique named cluster sampling, which guarantees the inclusion of nodes with diverse structural and influential properties in the training set. The sampling size is confined to a mere 0.5% of the complete network, leading to compact training sets, even in the case of expansive networks.…”

Section: Introductionmentioning

confidence: 99%

Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures

Hajarathaiah,

Enduri,

Anamalamudi

et al. 2024

IEEE Access

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The study addresses the limitations of traditional centrality measures in complex networks, especially in disease-spreading situations, due to their inability to fully grasp the intricate connection between a node's functional importance and structural attributes. To tackle this issue, the research introduces an innovative framework that employs machine learning techniques to evaluate the significance of nodes in transmission scenarios. This framework incorporates various centrality measures like degree, clustering coefficient, Katz, local relative change in average clustering coefficient, average Katz, and average degree (LRACC, LRAK, and LRAD) to create a feature vector for each node. These methods capture diverse topological structures of nodes and incorporate the infection rate, a critical factor in understanding propagation scenarios. To establish accurate labels for node significance, propagation tests are simulated using epidemic models (SIR and Independent Cascade models). Machine learning methods are employed to capture the complex relationship between a node's true spreadability and infection rate. The performance of the machine learning model is compared to traditional centrality methods in two scenarios. In the first scenario, training and testing data are sourced from the same network, highlighting the superior accuracy of the machine learning approach. In the second scenario, training data from one network and testing data from another are used, where LRACC, LRAK, and LRAD outperform the machine learning methods. INDEX TERMS Complex networks, influential nodes, local centralities, machine learning techniques

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“…Cheng et al [20] proposed an algorithm that performs the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank each node and take the node with the highest score as the seed. Rezaei et al [21] proposed a non-heuristic algorithm EML (Extended Machine Learning-based vital node identification), which makes use of the vitality of a part of a network for training a SVR model and predicts the vitality of each node based on this trained SVR.…”

Section: Seed Selectionmentioning

confidence: 99%

A local community detection algorithm based on potential community exploration

Wang

Yang²,

Ding³

et al. 2023

Front. Phys.

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Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC, that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.

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Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach

Cited by 5 publications

References 0 publications

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

HIKS: A K‐shell‐weighted hybrid approach method for detecting influential nodes in complex networks using possible edge weights

Node Significance Analysis in Complex Networks Using Machine Learning and Centrality Measures

A local community detection algorithm based on potential community exploration

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