Di Jin, Bunyamin Sisman, Hao Wei, Luna Xin Dong and Danai Koutra. Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation. Proceedings of the VLDB Endowment (PVLDB), 2022.
Di Jin, Ryan Rossi, Sungchul Kim and Danai Koutra. On Generalizing Static Node Embedding to Dynamic Settings. The Fifteenth International Conference on Web Search and Data Mining (WSDM), Phoenix, AZ, USA, Feb. 2022.
Nishil Talati, Di Jin, Haojie Ye, Ajay Brahmakshatriya, Saman Amarasinghe, Trevor Mudge, Danai Koutra, and Ronald Dreslinski. A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning. The the 2021 IEEE International Symposium on Workload Characterization (IISWC), Virtual conference, Nov. 2021.
Di Jin, Bunyamin Sisman, Hao Wei, Luna Xin Dong and Danai Koutra. Deep Transfer Learning for Multi-source Entity Linkage. Amazon Machine Learning Conference (AMLC), 2021, (oral paper - 10% acceptance rate)
Di Jin, Yingmin Luo, Zhongang Qi and Ying Shan. TransFusion: Multi-Modal Fusion for Video Tag Inference viaTranslation-based Knowledge Embedding. ACM MultiMedia (ACM MM), Chengdu, China, Oct. 2021.
Junchen Jin, Mark Heimann, Di Jin, and Danai Koutra. Towards Understanding and Evaluating Structural Node Embedding. Transactions on Knowledge Discovery from Data (TKDD) 2020.
Ryan Rossi, Di Jin, Sungchul Kim, Nerseen Ahmed, John Boaz Lee and Danai Koutra. From Community to Role-based Graph Embeddings. Transactions on Knowledge Discovery from Data (TKDD) pp. 36. 2020.
Di Jin, Mark Heimann, Ryan Rossi and Danai Koutra. node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Würzburg, Germany, Sep. 2019. [paper][poster][code]
Di Jin, Ryan Rossi, Eunyee Koh, Sungchul Kim, Anup Rao and Danai Koutra. Latent Network Summarization: Bridging Network Embedding and Summarization. ACM SIGKDD Conference of Knowledge Discovery and Data Mining (KDD), Anchorage, Aug. 2019. [paper][poster][code]
Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider and Danai Koutra. Smart Roles: Inferring Professional Roles in Email Networks. ACM SIGKDD Conference of Knowledge Discovery and Data Mining (KDD), Anchorage, Aug. 2019. [paper][poster][code]
Yujun Yan, Mark Heimann, Di Jin and Danai Koutra. Fast Flow-based Random Walk with Restart in a Multi-query Setting. SIAM International Conference on Data Mining (SDM), San Diego, May 2018.
Di Jin and Danai Koutra. Exploratory Analysis of Graph Data by Leveraging Domain Knowledge. IEEE International conference of data mining. (ICDM), New Orleans, November 2017. [paper][slides][code]
Di Jin, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, JunyueWu and Danai Koutra. PERSEUSHUB: Interactive and Collective Exploration of Large-Scale Graphs. Informatics 2017, 4, 22. (deployed system on Amazon AWS and Azure) [paper][code]
Di Jin, Mengdi Wang, Yu-Ru Lin. TELELINK: Link Prediction in Social Network Based on Multiplex Cohesive Structures. 2016 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), June 2016. [paper]
Workshop & Demo
Junchen Jin, Mark Heimann, Di Jin, Danai Koutra. Understanding and Evaluating Structural Node Embeddings. KDD Workshop on Mining and Learning with Graphs (MLG), August 2020.
Puja Trivedi, Alican Büyükçakır, Yin Lin, Yinlong Qian, Di Jin, Danai Koutra. On Structural vs. Proximity-based Temporal Node Embeddings. KDD Workshop on Mining and Learning with Graphs (MLG), August 2020.
Di Jin, Ryan Rossi, Eunyee Koh, Sungchul Kim, Anup Rao and Danai Koutra. Latent Network Summarization: Bridging Network Embedding and Summarization. KDD Workshop on Mining and Learning with Graphs (MLG), Anchorage, Aug. 2019.
Di Jin and Danai Koutra. Exploratory Analysis of Networks by Leveraging Domain Knowledge. International School and Conference on Network Science (NetSci’17), June 2017.
Di Jin, Christos Faloutsos, Danai Koutra, Ticha Sethapakdi. PERSEUS3: Visualizing and Interactively Mining Large-Scale Graphs. KDD Workshop on Mining and Learning with Graphs (MLG), August 2016. [paper][poster]
Danai Koutra, Di Jin, Yuanchi Ning, and Christos Faloutsos. PERSEUS: An Interactive Large-Scale Graph Mining and Visualization Tool. VLDB, Hawaii, September 2015. [paper]
