The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...
This work analyzes the connectivity of large diameter networks where every link has an independent probability p of failure. We give a (relatively simple) topological condition th...
We describe a methodology to examine bipartite relational data structures as exemplified in networks of corporate interlocking. These structures can be represented as bipartite gr...
Our work is motivated by the problem of ranking hyperlinked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based ...
Recently, quite a few papers studied methods for representing network properties by assigning informative labels to the vertices of a network. Consulting the labels given to any t...