Along with the rapid technological and commercial innovation on e-commerce platforms, an increasing number of frauds cause great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, no existing research can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds. Based on this, we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. In addition, we introduce a module namedTemporal Group Dynamics Analyzer(TGDA) that strengthens the ability to analyze temporal information on group fraudulent activity. Based on this, we built an enhanced model named TGFDN. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show our proposed model's superior effectiveness and efficiency for group-based fraud detection on bipartite graphs.

Nowadays, E-commercial platforms such as Taobao and Amazon, provide massive products which greatly improve the convenience of online shopping. However, there have been many redundant and lengthy titles, which are misleading and hurt the experience of consumers. Under this background, the product title compression task has been proposed to generate readable short titles. The existing supervised models rely on paired corpora, which is often expensive to annotate. Whereas unsupervised methods are not adopted in the E-commercial scenario due to lack of domain knowledge. To overcome the above limitations, we propose an unsupervised title compression method based on search behavior graph. Specifically, we build the knowledge graph extracted from search queries to detect redundancy. Moreover, click behaviors are utilized to give a clue to recognize inaccurate information. Then we employ a novel graph-to-text model consisting of an edge-importance encoder and a length-countdown decoder. The encoder combines the title with both query knowledge and click behaviors, and the decoder generates the compressed title with the desired length. Experiments on datasets show that our model outperforms the state-of-the-art models on both automatic and human evaluations. Moreover, further analysis demonstrates that our method has the ability to detect redundancy and keep the appropriate information.

Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.

Live-streaming platforms have recently gained significant popularity by attracting an increasing number of young users and have become a very promising form of online shopping. Similar to the traditional online shopping platforms such as Taobao, live-streaming platforms also suffer from online malicious fraudulent behaviors where many transactions are not genuine. The existing anti-fraud models proposed to recognize fraudulent transactions on traditional online shopping platforms are inapplicable on live-streaming platforms. This is mainly because live-streaming platforms are characterized by a unique type of heterogeneous live-streaming networks where multiple heterogeneous types of nodes such as users, live-streamers, and products are connected with multiple different types of edges associated with edge features. In this paper, we propose a new approach based on a heterogeneous graph neural network for LIve-streaming Fraud dEtection (called LIFE). LIFE designs an innovative heterogeneous graph learning model that fully utilizes various heterogeneous information of shopping transactions, users, streamers, and items from a given live-streaming platform. Moreover, a label propagation algorithm is employed within our LIFE framework to handle the limited number of labeled fraudulent transactions for model training. Extensive experimental results on a large-scale Taobao live-streaming platform demonstrate that the proposed method is superior to the baseline models in terms of fraud detection effectiveness on live-streaming platforms. Furthermore, we conduct a case study to show that the proposed method is able to effectively detect fraud communities for live-streaming e-commerce platforms.