As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

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Deep Learning for Community Detection: Progress, Challenges and Opportunities

Attention-based context-aware sequential recommendation model

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi‐selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ . Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist.

Semi-selfish mining based on hidden Markov decision process

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the richness of information has been greatly improved. However, these bands lead to increasing complexity of data processing, and the redundancy of adjacent bands is large. Recently, although many band selection methods have been proposed, this task is rarely handled through the context information of the whole spectral bands. Moreover, the scholars mainly focus on the different numbers of selected bands to explain the influence by accuracy measures, neglecting how many bands to choose is appropriate. To tackle these issues, we propose a fast neighborhood grouping method for hyperspectral band selection (FNGBS). The hyperspectral image cube in space is partitioned into several groups using coarse-fine strategy. By doing so, it effectively mines the context information in a large spectrum range. Compared with most algorithms, the proposed method can obtain the most relevant and informative bands simultaneously as subset in accordance with two factors, such as local density and information entropy. In addition, our method can also automatically determine the minimum number of recommended bands by determinantal point process. Extensive experimental results on benchmark data sets demonstrate the proposed FNGBS achieves satisfactory performance against state-of-the-art algorithms.

A Fast Neighborhood Grouping Method for Hyperspectral Band Selection

Is semi-selfish mining available without being detected?

Shared-nearest-neighbor-based clustering by fast search and find of density peaks

Network embedding is an important pre-process for analysing large scale information networks. Several network embedding algorithms have been proposed for unsigned social networks. However, these methods cannot be simply migrate to signed social networks which have both positive and negative relationships. In this paper, we present our signed social network embedding model which is based on the word embedding model. To deal with two kinds of links, we define two relationships: neighbour relationship and common neighbour relationship, as well as design a bias random walk procedure. In order to further improve interpretation of the representation vectors, the follow-proximally-regularized-leader online learning algorithm is introduced to the traditional word embedding framework to acquire sparse representations. Extensive experiments were carried out to compare our algorithm with three state-of-the-art methods for community detection and sign prediction tasks. The experimental results demonstrate that our algorithm performs better than the comparison algorithms on most signed social networks.

Sparse network embedding for community detection and sign prediction in signed social networks

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

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Xiaomei Yu

Papers

Shared-nearest-neighbor-based clustering by fast search and find of density peaks

Attention-based context-aware sequential recommendation model

Semi-selfish mining based on hidden Markov decision process

Sparse network embedding for community detection and sign prediction in signed social networks

Rational Protocols and Attacks in Blockchain System