We present path2vec, a new approach for learning graph embeddings that relies on
structural measures of pairwise node similarities. The model learns representations for …

This work investigates a somewhat different point of view on Markov decision processes by
reinterpreting them as a randomized shortest paths problem on a bipartite graph, therefore …

The present work investigates a new relative entropy-regularized algorithm for solving the
optimal transport on a graph problem within the randomized shortest paths formalism. More …

The emergence of networks and network data in different forms in the near past has given
rise to development of new data analysis methods with a shift in focus from vector spaces to …

The computation of distance measures between nodes in graphs is inefficient and does not
scale to large graphs. We explore dense vector representations as an effective way to …

Lexical semantics and world knowledge are crucial for interpreting bridging anaphora. Yet,
existing computational methods for acquiring and injecting this type of information into …

Network (or graph) data analysis finds application in many contexts, including biology,
finance, marketing, and physics, to name a few. With the recent thriving of internet and social …

Since the rapid growth of the Internet and the advent of social networks in the 2000s, the
amount of available network data is quickly increasing, leading to the development of new …

Establishing temporal order between events and resolving bridging references are crucial
for automatic discourse understanding. For that, effective event and mention representations …