Graph exploration

How can a user find information on a large graphs?

1 minute read

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…

1 minute read

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…

Similarities and embeddings

Does the graph structure conveys information on node / graph similarities?

1 minute read

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…