Enhanced density peak-based community detection algorithm

This thesis focuses on the pressing issue of illicit drug trafficking and its impact on public health and safety at a global level. With the advent of digital technologies and social media platforms, combating drug trafficking has become increasingly challenging for law enforcement and researchers alike. Among these platforms, Instagram, a popular photo and video-sharing social networking platform, has emerged as a prominent hub for drug trafficking activities. In this study, we delve into the effectiveness of community and key player detection algorithms in identifying and disrupting illicit drug supply networks on Instagram. To conduct our research, we collected real Instagram data spanning from June to August 2022. We examined several community detection algorithms, including Louvain, Newman-Girvan, Infomap, Label Propagation, and Hierarchical Clustering. Additionally, we explored key player algorithms, namely CDKPE, TopRank, K-core, and KPEI, the latter being a novel algorithm introduced in this study. Our objective was to assess the performance of these algorithms in accurately identifying and targeting drug trafficking networks. Our findings reveal that the Louvain and Newman-Girvan algorithms outperformed others in terms of community detection, demonstrating their effectiveness in identifying cohesive groups involved in drug trafficking on Instagram. In terms of key player detection, the CDKPE and KPEI algorithms emerged as the most effective, highlighting the individuals who play pivotal roles within these networks. These algorithms offer practical applications for law enforcement agencies seeking to disrupt drug trafficking operations on Instagram. By emphasizing the importance of leveraging advanced analytical tools, our study underscores the significance of combating drug trafficking on social media platforms.

Association rules are widely used to extract patterns from a given database. The association rules are capable of finding correlations among items, making it possible for the user to learn which items are present in the transactions and which of them have a significant correlation. One of the major problems with association rules is that the number of extracted rules usually exceeds the number of transactions present in the database, also surpassing the user’s capability to explore the obtained knowledge. To overcome this problem, the post-processing phase was proposed with the objective of directing the user to the rules that potentially have the most interesting knowledge. One of the used approaches is to divide the association rules into groups (or clusters), so that rules behave similarly are on the same group, facilitating the rule set understanding. In the literature, there are some works that uses clustering algorithms to split the rules while some other works use community detection algorithms. As both approaches obtain groups of association rules, but using different premises, different results can be obtained. No study has been done on the differences among clustering and community detection algorithms, which makes the selection of the algorithm hard, once their behavior is not well known in the association rule post-processing phase. This paper presents an analysis on both approaches, aiming to find the differences and the similarities among them, making it easier to select an approach by knowing its behavior.

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

Incorporating Community Detection and Clustering Techniques into Collaborative Filtering Model

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning and, in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

Community detection is crucial in the analysis of social networks. Its main goal is to find groups of users densely connected among them, and sparsely connected between themselves. There are so many algorithms which makes it difficult for researchers to choose the best one for a specific dataset. In this paper, we provide a thorough comparison between five Community Detection (CD) algorithms- Girman-Newman (GN), Clauset-Newman-Moore (CNM), Label Propagation Algorithm (LPA), Louvain and Leiden. To evaluate in real social network datasets like Zachary's Karate Club, Dolphin networks and bigger ones such as Facebook, Twitter, LinkedIn along with citation networks we used different metrics modularity and NMI. We employed Normalized Mutual Information (NMI) to quantify the agreement of detected communities with their true ground-truth score and modularity for evaluating the overall quality of partitions given by diverse methods. Our experiments show that greedy and modularity-optimization algorithms are particularly well-suited. Notably, the Leiden algorithm had a better modularity value than Louvain (Q = 0.9141 and Q = 0.9051 of LinkedIn Network respectively) in most of the dataset. The NMI plot provided more explanation about Clauset-Newman-Moore (CNM), Louvain and Label Propagation Algorithm (LPA)which are in good agreement on community detection and their NMI score. These results would allow us to choose more rationally among the different community detection algorithms for social network analysis, by providing accurate quantitative benchmarks.

Community reinforcement: An effective and efficient preprocessing method for accurate community detection

Segmentation is one of the most important subjects in image analysis due to its good performance in a wide range of applications. It is the task of clustering parts of an image together, which belong to the same object class. Using medical images for tumor growth modeling involves the improvement of all tasks of image processing, most importantly the segmentation. We introduce the tumor segmentation framework based on traditional machine learning and community detection algorithm. In This paper, we propose a novel approach for image segmentation, which is based on community detection algorithms existing in social networks. In this regard, we propose a method based on super-pixels and algorithms for community detection in graphs. The super-pixel method reduces the number of nodes in the graph while community detection algorithms provide more accurate segmentation than traditional approaches. We compare our method with the image segmentation method based on the deep learning approach and our previous work. Experimental results have shown that our method provides more precise segmentation.

<p dir="ltr">One of the most critical features of real-world networks is their community structure, as it reveals the internal organization of nodes. Finding such partitions is an interesting and essential task from both theoretical and practical perspectives. As a result, there is a growing demand for effective community detection algorithms. However, testing and designing such algorithms is demanding and challenging: one of the biggest problems is the lack of proper datasets and the fact that some have disputable ground-truth labeling, to say the least. One way to avoid this problem is to use artificial benchmarks with a built-in community structure.</p><p dir="ltr">In this work, we propose ABCDe—a multi-threaded implementation of the ABCD (Artificial Benchmark for Community Detection) graph generator. We test its performance and compare it with the previously available sequential version of the ABCD model and the parallel implementation of the standard and widely used LFR generator. We also investigate various theoretical asymptotic properties of the ABCD and ABCDe models. In particular, we analyze the modularity function, arguably the most important graph property in the context of community detection. By doing so, we show that models from the ABCD family are proper tools for designing new algorithms, not only those suited specifically for the community detection task.</p><p dir="ltr">Finally, we introduce a new approach to community detection in graphs. The idea is to initialize one of the popular heuristic-based community detection algorithms, such as Louvain or Leiden, with a partition obtained by embedding the graph nodes into a high-dimensional real vector space and then running a clustering algorithm on this latent representation. This procedure significantly improves the results and reduces the inherent volatility of such algorithms. Proper embedding filters out unnecessary information while retaining the proximity of nodes belonging to the same community.</p>

Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance.

Community detection in social networks based on fire propagation

Community structure is one of the most important properties in networks, and community detection has received an enormous amount of attention in recent years. Modularity is by far the most used and best known quality function for measuring the quality of a partition of a network, and many community detection algorithms are developed to optimize it. However, there is a resolution limit problem in modularity optimization methods. In this study, a memetic algorithm, named Meme-Net, is proposed to optimize another quality function, modularity density, which includes a tunable parameter that allows one to explore the network at different resolutions. Our proposed algorithm is a synergy of a genetic algorithm with a hill-climbing strategy as the local search procedure. Experiments on computer-generated and real-world networks show the effectiveness and the multiresolution ability of the proposed method.

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

The role of influential nodes and their influence domain in community detection: An approximate method for maximizing modularity

Many real world systems or web services can be represented as a network such as social networks and transportation networks. In the past decade, many algorithms have been developed to detect the communities in a network. However, the impact of locations on community has not been fully investigated by the research literature. In this paper, we propose a method to determine if a location-based community detection method is suitable for a given network and provide a new community detection algorithm that pushes the location information into the community detection. We test our proposed method on both synthetic data and real world network datasets. The results show that the communities detected by our method distribute in a smaller area compared with the traditional methods and have the similar or higher tightness on network connections.