Deep graph learning for anomalous citation detection

Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. © 2012 IEEE

Graph Force Learning

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning

Shifu2 : a network representation learning based model for advisor-advisee relationship mining

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE

Matching algorithms : fundamentals, applications and challenges

Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record*

CURRENT SITUATION INVESTIGATION AND COUNTERMEASURE RESEARCH OF STUDENTS’ ONLINE LEARNING DURING THE EPIDEMIC PERIOD: A CASE STUDY OF ZHEJIANG PROVINCE, CHINA

A survey of 538 students in 6 primary and secondary schools and colleges in Hangzhou, Ningbo and Jiaxing, Zhejiang Province, China has found: (1) Chinese schools suspended offline teaching in February-May, 2020 due to the novel coronavirus outbreak. All students studied online at home and 93% of them studied 2-7 hours a day online on average. Among all of them, students in primary schools spent least time online and college students spent most time. The science courses in middle school accounted for 46% of total studied courses, English accounted for 17%, and university major courses accounted for 21%. Furthermore, students spent 1-7 hours per day on watching TV and playing video games, and 1-4 hours on homework to review lessons. (2) After the end of the epidemic in China, more than 51% of students are still studying online for 1-4 hours a day, the epidemic situation has made online teaching in China popularized 10-20 years in advance, and students' online learning has become normal. (3) 32% of students like to study online, and they think that online class has the following advantages: numerous high-quality courseware that can be learned at any time anywhere, easy to communicate, save the time to go and from school, high learning efficiency, and online tutoring class charges are cheaper than offline ones. (4) The proportion of students who feel neutral and dislike the online study account for 56% and 9% respectively; they think online learning has the following problems: the online courses provided by schools are boring but they were forced to learn, and also have to clock in, which cannot bring the advantages of online education; the price of online tutoring course is very high; communication is not as easy as offline; the submission and correction of homework is more complicated than offline, and the learning effect is not good; students’ eyesight is decreased rapidly; online examination is not allowed. (5) 21% of parents are very supportive of online teaching, 62% of parents think it is acceptable, 17% of parents do not support or oppose, the reason for opposition is that their children do not have enough self-control, online learning effect is more difficult to ensure, eyesight loss is faster and so on. Therefore, the following countermeasures are put forward: (1) students are ought to be guided to pay attention to online learning; (2) to strengthen the reform of teaching methods, improve courseware quality, control teaching time, and leave students time for notes to ensure recess; (3) reduce video and broadcast courses, advocate live courses, strengthen the communication and interaction between teachers and students; (4) reform to simplify the online homework submission method, explore a reasonable online examination model; (5) strengthens the home-school cooperation, encourages the supervision function of parents, and strengthens the online teaching results. Article visualizations

A3Graph : adversarial attributed autoencoder for graph representation learning

Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM

Tracing the Pace of COVID-19 research : topic modeling and evolution

COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record*