I am a postdoctoral researcher in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. In 2020, I received my PhD in computer science from the University of Michigan, where I was a member of the GEMS Lab and advised by Danai Koutra. During my PhD I also completed research internships at the Information Sciences Institute, Adobe Research and Oak Ridge National Laboratory. I completed my undergraduate degree at Washington University in St. Louis in 2015.
My research interests are in data mining methods for large graphs or networks. During my PhD, I developed node embedding methods that capture structural roles of nodes in networks, along with principled and scalable formulations for graph mining problems such as network alignment and classification that are defined over multiple networks. You can read more about my work in technical detail in my dissertation, or more quickly consult a conceptual confectionary conspectus in my dessertation :) I am also interested in methodological connections and applications of to natural language processing and the social and natural sciences.
Publications
- Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS 2020.
- Mark Heimann, Goran Murić, and Emilio Ferrarra. Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. Complex Networks 2020. [code]
- Junchen Jin, Mark Heimann, Di Jin, and Danai Koutra. Understanding and Evaluating Structural Node Embedding. KDD Workshop on Mining and Learning with Graphs (MLG), 2020. Top submission, invited for contributed talk [code] [video]
- Xiyuan Chen, Mark Heimann, Fatemeh Vahedian, and Danai Koutra. CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding. CIKM 2020. Also accepted at KDD MLG Workshop 2020 [code] [video]
- Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann and Danai Koutra. G-CREWE: Graph CompREssion With Embedding for Network Alignment. CIKM 2020. [code]
- Mark Heimann, Tara Safavi, and Danai Koutra. Distribution of Node Embeddings as Multiresolution Features for Graphs. IEEE International Conference on Data Mining (ICDM), 2019. Best Student Paper [code]
- Di Jin, Mark Heimann, Ryan Rossi, and Danai Koutra. node2bits: Compact Time- and Attribute-aware
Node Representations for User Stitching. European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2019. [code]
- Di Jin*, Mark Heimann*, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, and Danai Koutra. Smart Roles: Inferring Professional Roles in Email Networks. Conference on Knowledge Discovery and Data Mining (KDD), 2019. [code]
- Mark Heimann, Haoming Shen, Tara Safavi, and Danai Koutra. REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management (CIKM), 2018. [code]
- Mark Heimann*, Wei Lee*, Shengjie Pan, Kuan-Yu Chen, and Danai Koutra. HashAlign: Hash-Based Alignment of Multiple Graphs. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. [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), 2018. [code]
- Mark Heimann and Danai Koutra. On Generalizing Neural Node Embedding Methods to Multi-Network Problems. KDD Workshop on Mining and Learning with Graphs (MLG), 2017.
*Equal contribution
