Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

Graph mining tasks arise from many different application domains, including social networks, biological networks, transportation, and E-commerce, which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph mining tasks. However, these fusion works are dispersed in different research domains, which makes them difficult to compare. In this survey, we provide a comprehensive overview of these fusion works and generalize these works to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains, and simultaneously propose the key challenges and advantages of integrating graph mining and RL methods. Furthermore, we propose important directions and challenges to be solved in the future. To our knowledge, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars. Based on our review, we create a collection of papers for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.

In recent years, a number of image encryption schemes based on DNA coding and nonlinear dynamics have been proposed. Generally, these DNA-based schemes first encode plaintext images into DNA sequences and then encrypt them with pseudorandom elements produced by chaotic systems or other nonlinear dynamics. Although ciphertexts can pass some security tests, many image encryption schemes are being shown to have intrinsic flaws and that they cannot guarantee a high level of security. In this paper, we cryptanalyze a family of image encryption schemes for which the encryption kernel is DNA coding or its variant. The complex DNA operation can be simplified as a substitution box (S-box). The whole cryptosystem's security level is thus significantly decreased and is vulnerable to the chosen-plaintext attack. Applications of this concept to break five ciphers are theoretically presented and experimentally verified. In addition, some suggestions for resisting similar attacks are also given in this paper.

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model. The BIRCH clustering algorithm is employed as the pre-algorithm of the autoencoder to pre-classify datasets with complex data distribution characteristics, while the autoencoder model is used to detect outliers based on a threshold. The proposed BIRCH-Autoencoder (BAE) algorithm has been tested on four network security datasets, KDDCUP99, UNSW-NB15, CICIDS2017, and NSL-KDD, and compared with representative algorithms. The BAE algorithm achieved average F-scores of 96.160, 81.132, and 91.424 on the KDDCUP99, UNSW-NB15, and CICIDS2017 datasets, respectively. These experimental results demonstrate that the proposed approach can effectively and accurately detect anomalous data.