The significance and impact of winning an academic award : a study of early career academics
- Authors: Ren, Jing , Shi, Yajie , Shatte, Adrian , Kong, Xiangjie , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022, Virtual, online, 20-24 June 2022, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
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- Description: Academic award plays an important role in an academic's careerparticularly for early career academics. Previous studies have primarilyfocused on the impact of awards conferred to academics whoe made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigatethe effect of awards conferred to academics at an earlier careerstage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluatethe change of a recipient's academic behavior after winningan academic award. Next, we propose a model to compare awardrecipients with academics who have similar performance beforewinning an academic award. In summary, we analyze the impact ofan award on the recipients' academic impact and their teams fromdifferent perspectives. Experimental results show that most recipientsdo have improvements in both productivity and citations afterwinning an academic award, while there is no significant impacton publication quality. In addition, receipt of an academic awardnot only expands recipients' collaboration network, but also has apositive effect on their team size. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
EAGLE : contrastive learning for efficient graph anomaly detection
- Authors: Ren, Jing , Hou, Mingliang , Liu, Zhixuan , Bai, Xiaomei
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Intelligent Systems Vol. 38, no. 2 (2023), p. 55-63
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- Description: Graph anomaly detection is a popular and vital task in various real-world scenarios, which has been studied for several decades. Recently, many studies extending deep learning-based methods have shown preferable performance on graph anomaly detection. However, existing methods lack efficiency that is definitely necessary for embedded devices. Toward this end, we propose an Efficient Anomaly detection model on heterogeneous Graphs via contrastive LEarning (EAGLE) by contrasting abnormal nodes with normal ones in terms of their distances to the local context. The proposed method first samples instance pairs on meta-path level for contrastive learning. Then, a Graph AutoEncoder-based model is applied to learn informative node embeddings in an unsupervised way, which will be further combined with the discriminator to predict the anomaly scores of nodes. Experimental results show that EAGLE outperforms the state-of-the-art methods on three heterogeneous network datasets. © 2001-2011 IEEE.
MIRROR : Mining implicit relationships via structure-enhanced graph convolutional networks
- Authors: Liu, Jiaying , Xia, Feng , Ren, Jing , Xu, Bo , Pang, Guanson , Chi, Lianhua
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 17, no. 4 (2023), p.
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- Description: Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights. © 2023 Association for Computing Machinery.