My research focuses on graph exploration, which lays on the broad areas of database, data mining, and machine learning.
In particular, I enjoy working and understanding graphs with or without labels interactively, understanding phenomena and users in a principled way.
I am looking for motivated and hard-working graduate and undergraduate students to work with me - email me your CV and your interest in the graph analytics area to join the group!
If you are interested in internships, please look here.
The increasing interest in social networks, knowledge graphs, protein-interaction, and many other types of networks has raised the question how users can explore such large and complex graph structures easily. In this regard, graph exploration has emerged as a complementary toolbox for graph management, graph mining, or graph visualization in which the user is a first class citizen. Graph exploration combines and expands database, data mining, and machine learning approaches with the user eye on one side and the system perspective on the other.
Search engines are continuously employing advanced techniques that aim to capture user intentions and provide results that go beyond the data that simply satisfy the query conditions. Examples include the personalized results, related searches, similarity search, popular and relaxed queries. In this work we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries «exemplar queries», and claim that they can play an important role in dealing with the information deluge. We provide a formal specification of the semantics of such queries and show that they are fundamentally different from notions like queries by example, approximate and related queries.
Graphs elegantly model a plethora of phenomena, from social interactions, to biological and chemical processes. Without any prior information comparing two graphs can only be done through notion of similarity. However, designing the right similarity for a certain task is a convolotued process which requires domain expertise and graph expertise. We propose self-learned similarity measures which scale to large graphs and are automatically harvested from the graph itself. Such measures can outperform traditional measures in different tasks, such as predicting future connections among users, understanding the effect of drugs, and detecting malicious users.