Detection In Dynamic Networks Research Articles

Influence detection in dynamic networks: a novel overlapping community detection approach applied to COVID-19 spread analysis in India

Influence detection in dynamic networks: a novel overlapping community detection approach applied to COVID-19 spread analysis in India

Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networks

Dynamic networks are ubiquitous in many domains for modelling evolving graph-structured data and detecting changes allows us to understand the dynamic of the domain represented. A category of computational solutions is represented by the pattern-based change detectors (PBCDs), which are non-parametric unsupervised change detection methods based on observed changes in sets of frequent patterns over time. Patterns have the ability to depict the structural information of the sub-graphs, becoming a useful tool in the interpretation of the changes. Existing PBCDs often rely on exhaustive mining, which corresponds to the worst-case exponential time complexity, making this category of algorithms inefficient in practice. In fact, in such a case, the pattern mining process is even more time-consuming and inefficient due to the combinatorial explosion of the sub-graph pattern space caused by the inherent complexity of the graph structure. Non-exhaustive search strategies can represent a possible approach to this problem, also because not all the possible frequent patterns contribute to changes in the time-evolving data. In this paper, we investigate the viability of different heuristic approaches which prevent the complete exploration of the search space, by returning a concise set of sub-graph patterns (compared to the exhaustive case). The heuristics differ on the criterion used to select representative patterns. The results obtained on real-world and synthetic dynamic networks show that these solutions are effective, when mining patterns, and even more accurate when detecting changes.

VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks

VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks

Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks

Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks

Generative Evolutionary Anomaly Detection in Dynamic Networks

Generative Evolutionary Anomaly Detection in Dynamic Networks

A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

Graph similarity learning for change-point detection in dynamic networks

Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectivity, population migrations, and social networks. In this work, we consider the discrete-time framework of dynamic networks and aim at detecting change-points, i.e., abrupt changes in the structure or attributes of the graph snapshots. This task is often termed network change-point detection and has numerous applications, such as market phase discovery, fraud detection, and activity monitoring. In this work, we propose a data-driven method that can adapt to the specific network domain, and be used to detect distribution changes with no delay and in an online setting. Our algorithm is based on a siamese graph neural network, designed to learn a graph similarity function on the graph snapshots from the temporal network sequence. Without any prior knowledge on the network generative distribution and the type of change-points, our learnt similarity function allows to more effectively compare the current graph and its recent history, compared to standard graph distances or kernels. Moreover, our method can be applied to a large variety of network data, e.g., networks with edge weights or node attributes. We test our method on synthetic and real-world dynamic network data, and demonstrate that it is able to perform online network change-point detection in diverse settings. Besides, we show that it requires a shorter data history to detect changes than most existing state-of-the-art baselines.

An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering

PurposeCommunity detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.Design/methodology/approachThis paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.FindingsExperimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.Originality/valueTo enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

A deep co-evolution architecture for anomaly detection in dynamic networks

A deep co-evolution architecture for anomaly detection in dynamic networks

Change Point Detection in Dynamic Networks via Regularized Tensor Decomposition

Dynamic network captures time-varying interactions among multiple entities at different time points, and detecting its structural change points is of central interest. This article proposes a novel method for detecting change points in dynamic networks by fully exploiting the latent network structure. The proposed method builds upon a tensor-based embedding model, which models the time-varying network heterogeneity through an embedding matrix. A fused lasso penalty is equipped with the tensor decomposition formulation to estimate the embedding matrix and a power update algorithm is developed to tackle the resultant optimization task. The error bound of the obtained estimated embedding matrices is established without incurring the computational-statistical gap. The proposed method also produces a set of estimated change points, which, coupled with a simple screening procedure, assures asymptotic consistency in change point detection under much milder assumptions. Various numerical experiments on both synthetic and real datasets also support its advantage. Supplementary materials for this article are available online.

AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder

Recently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology. However, many existing methods in this area fail to fully leverage all available information from dynamic networks. Additionally, most of these methods are supervised or semi-supervised algorithms that require labeled data, which may not always be feasible in real-world scenarios. In this paper, we propose AddAG-AE, a general dynamic graph anomaly-detection framework that can fuse node attributes and spatiotemporal information to detect anomalies in an unsupervised manner. The framework consists of two main components. The first component is a feature extractor composed of a dual autoencoder, which captures a joint representation of both the network structure and node attributes in a latent space. The second component is an anomaly detector that combines a Long Short-Term Memory AutoEncoder (LSTM-AE) and a predictor, effectively identifying abnormal snapshots among most normal graph snapshots. Compared with baselines, experimental results show that the method proposed has broad applicability and higher robustness on three datasets with different sparsity.

Local Community Detection in Graph Streams with Anchors

Community detection in dynamic networks is a challenging research problem. One of the main obstacles is the stability issues that arise during the evolution of communities. In dynamic networks, new communities may emerge and existing communities may disappear, grow, or shrink. As a result, a community can evolve into a completely different one, making it difficult to track its evolution (this is known as the drifting/identity problem). In this paper, we focused on the evolution of a single community. Our aim was to identify the community that contains a particularly important node, called the anchor, and to track its evolution over time. In this way, we circumvented the identity problem by allowing the anchor to define the core of the relevant community. We proposed a framework that tracks the evolution of the community defined by the anchor and verified its efficiency and effectiveness through experimental evaluation.

Online monitoring of dynamic networks using flexible multivariate control charts

Change-point detection in dynamic networks is a challenging task which is particularly due to the complex nature of temporal graphs. Existing approaches are based on the extraction of a network’s information by the reduction to a model or to a single metric. Whereas the former one requires restrictive assumptions and has limited applicability for real-world social networks, the latter one may suffer from a huge information loss. We demonstrate that an extension to a well-balanced multivariate approach that uses multiple metrics jointly to cover the relevant network information can overcome both issues, since it is applicable to arbitrary network shapes and promises to strongly mitigate the information loss. In this context, we give guidelines on the crucial questions of how to properly choose a suitable multivariate metric set together with the choice of a meaningful parametric or nonparametric control chart and show that an improper application may easily lead to unsatisfying results. Furthermore, we identify a solution that achieves reasonable performances in flexible circumstances in order to give a reliably applicable approach for various types of social networks and application fields. Our findings are supported by the use of extensive simulation studies, and its applicability is demonstrated on two real-world data sets from economics and social sciences.

Multi-view change point detection in dynamic networks

Change point detection aims to find the locations of sudden changes in the network structure, which persist with time. However, most current methods usually focus on how to accurately detect change points, without providing deeper insight into the cause of the change. In this study, we propose the multi-view feature interpretable change point detection method (MICPD), which is based on a vector autoregressive (VAR) model to encode high-dimensional network data into a low-dimensional representation, and locate change points by tracking the evolution of multiple targets and their interactions across the whole timeline. According to the evolutionary nature of dynamic networks, we define a categorization of different types of changes which can occur in dynamic networks. We compare the performance of our method with state-of-the-art methods on four synthetic datasets and the world trade dataset. Experimental results show that our method achieves well in most cases.

An enforced non-negative matrix factorization based approach towards community detection in dynamic networks

Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.

Density-based structural embedding for anomaly detection in dynamic networks

Dynamic networks continuously change their structure and properties, and anomaly detection methods must identify these structural changes at both local and global levels. Network embedding is considered a powerful tool for low-dimension structural representation of network objects. However, most embedding techniques developed for dynamic networks are incompetent in capturing the changes in global structures. Besides, with incoming edge streams, these techniques change the embeddings of all network objects, including those vertices and edges that do not experience any change in subsequent timestamps. In this paper, we propose an embedding algorithm DSEDN to identify anomalous vertices and edges in dynamic networks utilizing structural changes in networks. The algorithm uses sparse autoencoder to generate network embeddings minimizing the pair-wise and neighborhood distance between vertex representations of every subgraph derived from random walks. The subgraphs have been weighted based on their respective clustering coefficient, and the clustering algorithm is employed to identify network anomalies. The advantages of proposed method include: (i) better accuracy in detecting network anomalies; (ii) structure-preserving embeddings, such that it maintains the local and global structure of every snapshot of growing graphs; (iii) density-based embeddings; (iv) stable embeddings over time such that embeddings of consecutive timestamps do not change much for those network objects that do not experience any change; (v) scalability. We evaluate the proposed algorithm on anomaly detection and graph visualization on five real-world datasets. Our algorithm achieves improvement in AUC, scalability, and stability across all baselines.

Dynamic community detection based on the Matthew effect

The identification of community structures plays a crucial role in analyzing network topology, exploring network functions, and mining potential patterns in complex networks. Many algorithms have been proposed for identifying community structures in static networks from different perspectives. However, most networks in the real world are not static and their structures constantly evolve over time. Identifying community structures in dynamic networks remains a challenging task because of the variability, complexity, and large scale of dynamic networks. In this study, we propose a framework and Matthew effect model for community detection in dynamic networks. Based on this architecture and model, we design a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot. DCDME has several desirable benefits: high-quality community detection, parameter-free operation, and good scalability. Extensive experiments on synthetic and real-world dynamic networks have demonstrated that DCDME has many advantages and outperforms several state-of-the-art algorithms.

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

Combining Information from Multiple Views for Vertex-Based Change Detection in Dynamic Networks: A Comparative Study

Combining Information from Multiple Views for Vertex-Based Change Detection in Dynamic Networks: A Comparative Study

Incremental methods for community detection in both fully and growing dynamic networks

Abstract In recent years, community detection in dynamic networks has received great interest. Due to its importance, many surveys have been suggested. In these surveys, the authors present and detail a number of methods that identify a community without taking into account the incremental methods which, in turn, also take an important place in dynamic community detection methods. In this survey, we provide a review of incremental approaches to community detection in both fully and growing dynamic networks. To do this, we have classified the methods according to the type of network. For each type of network, we describe three main approaches: the first one is based on modularity optimization; the second is based on density; finally, the third is based on label propagation. For each method, we list the studies available in the literature and state their drawbacks and advantages.