An Analysis on Community Detection and Clustering Algorithms on the Post-Processing of Association Rules

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.

Incorporating Community Detection and Clustering Techniques into Collaborative Filtering Model

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%.

Cluster structure detection of the network is a basic problem of complex network analysis. This study investigates the structure of the value migration network using data from 499 stocks listed in the S&P500 as of the end of 2021. An examination is carried out whether the process of value migration creates a cluster structure in the network of companies according to economic activity. Specifically, the cohesion and segregation of the extracted modules in the network division according to (i) sector classification, (ii) community division, and (iii) network clustering decomposition are assessed. The results of this study show that the sector classification of the value migration network has a non-cohesive structure, which means that the flow of value in the financial market occurs between companies from various industries. Moreover, the divisions of the value migration network based on community detection and clustering algorithm are characterized by intra-cluster similarity between the vertices and have a strong community structure. The structure of the network division into modules corresponding to the classification of economic sectors differs significantly from the partition based on the algorithms applied.

We examine the problem of effective identification of community structure of a network whose elements and their respective relationships manifest through streams. The problem has recently garnered much interest as it appears in emerging computational environments and concerns critical applications in diverse areas including social computing, web analysis, IoT and biology. Despite the already expended research efforts in detecting communities in networks, the unprecedented volume that real-world networks now reach, renders the task of revealing community structures extremely burdensome. The sheer size of such networks oftentimes makes their representation in main memory impossible. Thus, processing the developing graphs to extract the underlying communities remains an open challenge. In this paper, we propose a graph-stream community detection algorithm that expands seed-sets of nodes to communities. We consider a stream of edges and aim at processing them to form communities without maintaining the entire graph structure. Instead, we maintain very limited information regarding the nodes of the graph and the communities we seek. In addition to our novel streaming approach, we both develop a technique that increases the accuracy of our algorithm considerably and propose a new clustering algorithm that allows for automatically deriving the size of the communities we seek to detect. Our experimental evaluation using ground-truth communities for a wide range of large real-word networks shows that our proposed approach does achieve accuracy comparable or even better to that of state-of-the-art non-streaming community detection algorithms. More importantly, the attained improvements in both execution time and memory space requirements are remarkable.

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.

Community detection is essential to various graph analysis applications. Infomap is a graph clustering algorithm capable of achieving high-quality communities. However, it remains a very challenging problem to effectively apply Infomap on large graphs. By analyzing communication and workload patterns of Infomap and leveraging a distributed delegate partitioning and distribution method, we develop a new heuristic strategy to carefully coordinate the community constitution from the vertices of a graph in a distributed environment, and achieve the convergence of the distributed clustering algorithm. We have implemented our optimized algorithm using MPI (Message Passing Interface), which can be easily employed or extended to massively distributed computing systems. We analyze the correctness of our algorithm, and conduct an intensive experimental study to investigate the communication and computation cost of our distributed algorithm, which has not shown in previous work. The results demonstrate the scalability and the correctness of our distributed Infomap algorithm with large-scale real-world datasets.

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

<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>

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.

One of the most relevant and widely studied structural properties of networks is their community structure or clustering. Detecting communities is of great importance in various disciplines where systems are often represented as graphs. Different community detection algorithms have been introduced in the past few years, which look at the problem from different perspectives. Most of these algorithms, however, have expensive computational time that makes them impractical to use for large graphs found in the real world. Maintaining a good balance between the computational time and the quality of the communities discovered is a well-known open problem in this area. In this paper, we propose a multi-core multi-level (MCML) community detection algorithm based on the topology of the graph, which contributes towards solving the above problem. MCML algorithm on two benchmark datasets results in detection of accurate communities. We detect high modularity communities by applying MCML on Facebook Forum dataset to find users with similar interests and Amazon product dataset. We also show the scalability of MCML on these large datasets with 16 Xeon Phi cores.

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

In the real network, there must be a large and complex network. The solution to understand that kind of network structure is using the community detection algorithms. There are a lot of other algorithms out there to perform community detection. Each of the algorithms has its own advantages and disadvantages with different types and scale of complex network. The Louvain has been experimented that shows bad connected in community and disconnected when running the algorithm iteratively. In this paper, two algorithm based on agglomerative method (Louvain and Leiden) are introduced and reviewed. The concept and benefit are summarized in detail by comparison. Finally, the Leiden algorithm’s property is considered the latest and fastest algorithm than the Louvain algorithm. For the future, the comparison can help in choosing the best community detection algorithms even though these algorithms have different definitions of community.