Community Detection In Large Networks Research Articles

Deterrence or Coercion: Analyzing DPRK’s Nuclear Intentions with Its Propaganda Texts

Although the Democratic People’s Republic of Korea’s (DPRK) nuclear program has been a challenge to international security for three decades, the world still knows little about its nuclear intentions. A recently available dataset of Korean Central News Agency’s English publications (1997–2018) provides a complete coverage of DPRK’s nuclear activities in the time span. Our study employs the Louvain method of community detection in large networks to detect patterns and trends in North Korea’s nuclear rhetoric. We have two findings: Pyongyang’s primary objective is deterrence, although it also utilizes nuclear development to boost regime legitimacy. This secondary intention of legitimization is more prominent under Kim Jong-un than under Kim Jong-il, but still not as salient as deterrence. Our results suggest a policy approach of engagement and deterrence.

Enhancing Sparse Adjacency Matrix for Community Detection in Large Networks

This paper presents a problem of community detection in sparse network. Graph represents the network with {0, 1} symmetric matrix, this matrix is defined to be sparse when most of its entries are zeros. The problem of community detection of this type of networks is non-deterministic polynomial-time hardness (NP-hard) problem. Here, we give a simple idea to regularize the sparse matrix by adding a heuristic parameter to the entries of the matrix. This work performs integrating Tabu Search via Fuzzy C-mean to compute variants of the modularity maximization. The results show the ability of the proposed method to define structure of the network by optimizing different types of the quality functions; the results show the global function gives the high value in must runs when apply it on a large sparse real networks.

Krylov Subspace Approximation for Local Community Detection in Large Networks

Community detection is an important information mining task to uncover modular structures in large networks. For increasingly common large network datasets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities,  i.e., identifying all latent members of a particular community from a few labeled seed members. To address such semi-supervised mining task, we systematically develop a local spectral (LOSP) subspace-based community detection method, called LOSP. We define a family of LOSP subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an ℓ 1 norm minimization over the Krylov subspace. Variants of LOSP depend on type of random walks with different diffusion speeds, type of random walks, dimension of the LOSP subspace, and step of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and it is experimentally verified on a wide variety of real-world networks across social, production, and biological domains, as well as on an extensive set of synthetic LFR benchmark datasets.

Label propagation method based on bi-objective optimization for ambiguous community detection in large networks

Community detection is of great significance because it serves as a basis for network research and has been widely applied in real-world scenarios. It has been proven that label propagation is a successful strategy for community detection in large-scale networks and local clustering coefficient can measure the degree to which the local nodes tend to cluster together. In this paper, we try to optimize two objects about the local clustering coefficient to detect community structure. To avoid the trend that merges too many nodes into a large community, we add some constraints on the objectives. Through the experiments and comparison, we select a suitable strength for one constraint. Last, we merge two objectives with linear weighting into a hybrid objective and use the hybrid objective to guide the label update in our proposed label propagation algorithm. We perform amounts of experiments on both artificial and real-world networks. Experimental results demonstrate the superiority of our algorithm in both modularity and speed, especially when the community structure is ambiguous.

Greedy Modularity Graph Clustering for Community Detection of Large Co-Authorship Network

Social networks as a domain of complex networks that can be represented as graphs according to the patterns of connections among their elements. Social Communities are a set of nodes with denser connections inside community structures than outside. The goal of graph clustering is to divide the large graph into many clusters depending on multiple similarity criteria. In this work an improved version of the Louvain method is proposed, the Greedy Modularity Graph Clustering for Community Detection of Large Co-AuthorshipNetwork (GMGC)which introduces a new concept of weighted edges to enhance the accuracy of the Community Discovery for the large networks. The method is compared with other states of art methods mainly, Vertices Similarity First and Community Mean (VSFCM), and Generalized Louvain method for community detection in large networks (FKCD). Extensive experimental results have been madeon different datasets. The experimental results showed that the proposed method outperforms the other states of arts comparative methods according to the modularity optimization and community partitions evaluations measures.   

Compressing Networks with Super Nodes

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of ‘super nodes’, where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the ‘CoreHD’ ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network.

A Framework for Community Detection in Large Networks Using Game-Theoretic Modeling

Community detection is a fundamental component of large network analysis. In both academia and industry, progressive research has been made on problems related to community network analysis. Community detection is gaining significant attention and importance in the area of network science. Regular and synthetic complex networks have motivated intense interest in studying the fundamental unifying principles of various complex networks. This paper presents a new game-theoretic approach towards community detection in large-scale complex networks based on modified modularity; this method was developed based on modified adjacency, modified Laplacian matrices and neighborhood similarity. This approach was used to partition a given network into dense communities. It is based on determining a Nash stable partition, which is a pure strategy Nash equilibrium of an appropriately defined strategic game in which the nodes of the network were the players and the strategy of a node was to decide to which community it ought to belong. Players chose to belong to a community according to a maximized fitness/payoff. Quality of the community networks was assessed using modified modularity along with a new fitness function. Community partitioning was performed using Normalized Mutual Information and a ‘modularity measure’, which involved comparing the new game-theoretic community detection algorithm (NGTCDA) with well-studied and well-known algorithms, such as Fast Newman, Fast Modularity Detection, and Louvain Community. The quality of a network partition in communities was evaluated by looking at the contribution of each node and its neighbors against the strength of its community.

A framework for detecting communities of unbalanced sizes in networks

Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

A weighted local view method based on observation over ground truth for community detection

Community detection is a fundamental problem for many networks, and there have been a lot of methods proposed to discover communities. However, due to the rapid increase of the scale and diversity of networks, the modular organization at the global level in many large networks is often extremely difficult to recognize. In these cases, many existing methods fail to discover the latent community structure, because they follow a paradigm of discovering communities from a global view of networks. In this paper, we propose a weighted local view method based on an interesting observation on ground-truth communities, with the aim of revealing community structure in large real networks. This is achieved by the following steps: 1) a set of nodes which can well represent their neighboring nodes are chosen by local seeding strategies; 2) each chosen node explores the community in its local view to the whole network, using an improved approximate personalized PageRank-based community finder which is based on an interesting observation on large real networks with ground-truth communities; 3) all explored local communities are merged to form the global community structure. We evaluate the weighted local view method against the state-of-the-art community detection methods on large real networks with ground-truth communities. Experiments show that the proposed method can not only improve the detected communities, but can also scale to very large networks with good computational efficiency compared with other methods, which indicates that the weighted local view method has great potential for overlapping community detection in large networks.

A New Randomized Algorithm for Community Detection in Large Networks

The problem of community detection (or clustering) in graphs plays an important role in analysis of complex large-scale networks and big data structures, arising in natural, behavioral and engineering sciences. Examples of such networks include, but are not limited to, World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph’s partitions of maximal modularity. One of the most efficient graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph’s clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.

A smart local moving algorithm for large-scale modularity-based community detection

We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the local moving heuristic in a more sophisticated way. Based on an analysis of a diverse set of networks, we show that our smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular “Louvain algorithm”. The computational efficiency of our algorithm makes it possible to perform community detection in networks with tens of millions of nodes and hundreds of millions of edges. Our smart local moving algorithm also performs well in small and medium-sized networks. In short computing times, it identifies community structures with modularity values equally high as, or almost as high as, the highest values reported in the literature, and sometimes even higher than the highest values found in the literature.

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.

Towards real-time community detection in large networks

The recent boom of large-scale online social networks (OSNs) both enables and necessitates the use of parallelizable and scalable computational techniques for their analysis. We examine the problem of real-time community detection and a recently proposed linear time- O(m) on a network with m edges-label propagation, or "epidemic" community detection algorithm. We identify characteristics and drawbacks of the algorithm and extend it by incorporating different heuristics to facilitate reliable and multifunctional real-time community detection. With limited computational resources, we employ the algorithm on OSN data with 1 x 10(6) nodes and about 58 x 10(6) directed edges. Experiments and benchmarks reveal that the extended algorithm is not only faster but its community detection accuracy compares favorably over popular modularity-gain optimization algorithms known to suffer from their resolution limits.