Modeling of Network Processes

Graphs have evolved into a rich framework for the solution of diverse problems, ranging from mathematical puzzles to the analysis of planetary-scale social networks. A particular class of problems that has attracted recent interest in the research community is related to how to model information that is embedded in a graph structure. In this scenario, the graph dynamics is guided by processes that are localized both in time and space, with effects at different scales. For instance, traffic in road networks and content popularity on the Web are two scenarios where understanding the role played by network processes (e.g. traffic jams, information diffusion) might lead to new models, algorithms, and data structures for managing large dynamic graphs. We are developing methods for modeling and analyzing network processes using ideas from graph theory, information theory, and sampling.

Selected Publications

Affiliated People

Victor Amelkin

Research interests:

Network Science, Social and Economic Networks, Algorithms, Data Mining, Systems and Control.

Victor graduated with a PhD from UCSB's Department of Computer Science in June, 2018, having worked on network processes with an emphasis on social and economic networks. In July, 2018, he joined the University of Pennsylvania's Warren Center for Network and Data Sciences as a Postdoctoral Fellow, where he worked until mid-2020, prior to joining Amazon.com as a Research Scientist. Before joining Dynamo Lab, Victor worked in scientific computing, and spent several years as a software engineer in industry. He also holds a MSc and a BSc degrees in Applied Mathematics and Computer Science.

Haraldur Hallgrimsson

Research interests:

Data science, network science, machine learning, brain networks

Haraldur recieved a B.Sc in Electrical Engineering and a B.Sc in Computer Science at the University of Iceland in 2013 and 2014, respectively. His Ph.D. research is focused on statistical and machine learning methods to model and understand the human brain from MRI data. Prior to joining Dynamo in 2014 he developed methods to diagnose sleep disorders, and since joining Haraldur has studied deep learning architectures at Google Research and the effect social networks have on physical behavior at Evidation Health, among other projects.

Zexi Huang

Research interests:

Graph Data Mining, Representation Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. His research interests span the analysis of social, informational, and biological networks with machine learning and data mining techniques.

Mert Kosan

Research interests:

Data Mining, Applied Machine Learning, Network Science.

Mert received a B.Sc in Computer Science and Engineering in 2018 from Sabanci University, Istanbul. He joined Dynamo lab as a Ph.D. student in 2018. His research interests include machine learning on graphs, explainability, and human-ai interactions. He is exploring novel graph neural network algorithms to solve graph tasks such as graph classification and link prediction on the different types of networks like social, communication, and molecular. He previously worked on differential privacy on recommendation systems using graph data in his bachelor's.

Sikun Lin

Research interests:

graph mining, brain networks, computer vision

Sikun received her B.Sc degree in Physics and Math from Hong Kong University of Science and Technology in 2015 and her MPhil degree in Computer Science from the same university in 2017.

Zhao Tang

Research interests:

Network science, machine learning

4th-year undergraduate CS student. Interested in applying ML methods to graph and network problems. Currently working on the Eigen Path problem, which aims to use neural networks to compactly represent networks flows that are generated with persistent paths.

Wei Ye

Research interests:

Machine learning, data mining and network science. Specifically, clustering, semi-supervised learning, classification, relational learning, and causal reasoning.

Wei is a postdoctoral researcher with the DYNAMO lab. He received his Dr.rer.nat (PhD) degree in Computer Science from Lugwig-Maximilians University of Munich in 2018. Before joining the DYNAMO lab, he worked as a researcher in the Department of AI Platform, Tencent Inc. China.