A community detection based approach for Service Function Chain online placement in data center network

Zu, Jiachen; Hu, Guyu; Yan, Jiajie; Tang, Siqi

doi:10.1016/j.comcom.2021.01.014

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…Each of the past research initiatives analysed the network's content from a unique perspective and then used this analysis to discover communities by considering the topological information of a network (Ullah et al, 2022). Recent trends in the practical application of community detection in network structure especially using the Louvain and Leiden algorithm, are in healthcare (Chatterjee & Sanjeev, 2023;Evans et al, 2022;Jin et al, 2020;Kabir et al, 2019;Kramer et al, 2020;Nallusamy & Easwarakumar, 2023;Nicolini et al, 2017;Rahiminejad et al, 2019), social network (Chessa et al, 2023;Irsyad & Rakhmawati, 2019;Li et al, 2023;Park & Kwon, 2022;Torene et al, 2022), telecommunication (Ding et al, 2022;Zu et al, 2021), economic (Han et al, 2018;, intelligent (Karyotis et al, 2018;Singhal et al, 2020) and nature (Peeples & Bischoff, 2023;Xie et al, 2022).…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Hairol Anuar,

Abal Abas,

Md Yunos

et al. 2024

JST

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Over the past 20 years, there has been a significant increase in publication in complex network analysis research, especially in community detection. Many methods were proposed to identify community structure. Each community identification algorithm has strengths and weaknesses due to the complexity of information. Among them, the optimisation methods are widely focused on. This paper focuses on an empirical study of two community detection algorithms based on agglomerative techniques using modularity metric: Louvain and Leiden. In this regard, the Louvain algorithm has been shown to produce a bad connection in the community and disconnected when executed iteratively. Therefore, the Leiden algorithm is designed to successively resolve the weaknesses. Performance comparisons between the two and their concept were summarised in detail, as well as the step-by-step learning process of the state-of-the-art algorithms. This study is important and beneficial to the future study of interdisciplinary data sciences of network analysis. First, it demonstrates that the Leiden method outperformed the Louvain algorithm in terms of modularity metric and running time. Second, the paper displays the use of these two algorithms on synthetic and real networks. The experiment was successful as it identified better performance, and future work is required to confirm and validate these findings.

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Section: Hierarchical Clusteringmentioning

confidence: 99%

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Hairol Anuar,

Abal Abas,

Md Yunos

et al. 2024

JST

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“…On the other side, edge betweenness-based methods rely on edge betweenness centrality, which is defined as the number of shortest paths passing through an edge in a network for community detection. The GN method [10] proposes removing edges with high betweenness to identify communities. Gregory et al [30] extended this approach for overlapping community detection by introducing the concept of split betweenness to refine the identification of intercommunity edges.…”

Section: Related Workmentioning

confidence: 99%

“…In neurobiology, community detection sheds light on the functional dynamics of neuronal networks. Similarly, data center networks leverage community detection for the efficient placement of online service function chains [10]. Therefore, community detection has garnered massive attention in the research community in the first research work two decades ago.…”

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confidence: 99%

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Robust Overlapping Community Detection in Complex Networks With Graph Convolutional Networks and Fuzzy C-Means

Al-Andoli,

Irianto,

Alsayaydeh

et al. 2024

IEEE Access

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Community detection is an important task in complex network analysis. A community is a set of cohesive vertices that have more connections within the set than outside. In many real Complex Networks (CNs), these communities naturally overlap, meaning an individual node can belong to more than one community. This overlapping structure is crucial for many real applications, such as social influence detection, cyberattack detection, and recommendation systems. Existing methods often struggle to capture both network topology and node features, leading to suboptimal overlapping community detection. In this paper, we propose an efficient method called GCNFCM, which utilizes Graph Convolutional Networks (GCNs), Fuzzy C-means (FCM), and the modularity Q algorithm for overlapping community detection. The key idea is to achieve robust feature learning for nodes and then identify the best structure for overlapping community detection. GCNFCM extracts node embeddings from CNs, considering both topology and attributes through a dual-decoder design (inner product and GCN), while FCM is employed for optimal overlapping community detection. Furthermore, FCM is guided by the modularity Q algorithm for accurate community identification without requiring prior knowledge of the community count. Experimental results on ten real-world CNs of varying sizes demonstrate that our proposed method outperforms other state-of-theart overlapping community detection methods in terms of producing cohesive communities and identifying ground-truth communities. Additionally, the results indicate that the developed method effectively identifies good overlapping communities in real-world networks. INDEX TERMSGraph Convolutional Networks, Fuzzy C-means, Complex networks. Overlapping Structure, Community detection. I. INTRODUCTIONNowadays, with the rapid expansion of information technology, things in the real world are more connected than ever because and usually represented as complex networks [1]. Complex Networks (CN) are distinguished by intricate interconnections, which serve as a powerful analytical tool for understanding a variety of interconnected systems in the real world. These systems encompass diverse domains such as the internet, biological neural networks, transportation systems, and social networks [1,2]. Complex networks like social media, protein interactions, and city routes are difficult to understand because they have complicated structures and keep changing. In real-world networks, connected groups are called "communities", which share common things. Identifying these communities is crucial for understanding network structures, and is valuable in different areas like biology, sociology, and computer science. For example, in computer science, finding

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“…Most of them are based on knowledge of traffic and solving optimization problems and/or heuristics. Offline [15] and Online [16,17] algorithms have been considered. When traffic variations occur, resource reconfiguration algorithms [18] have been proposed.…”

Section: Related Work and Research Contributionmentioning

confidence: 99%

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Eramo¹,

Valente²,

Catena³

et al. 2021

Future Internet

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Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.

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A community detection based approach for Service Function Chain online placement in data center network

Cited by 11 publications

References 20 publications

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Robust Overlapping Community Detection in Complex Networks With Graph Convolutional Networks and Fuzzy C-Means

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

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