In this work we address algorithmic fairness concerns that arise when graph nodes are
ranked based on their structural relatedness to a personalized set of query nodes. In …

Community structure detection techniques play a prominent role in analysing complex
networks, emerging from diverse domains. A major reason why this area is flourishing is that …

We introduce pygrank, an open source Python package to define, run and evaluate node
ranking algorithms. We provide object-oriented and extensively unit-tested algorithmic …

A recurring graph analysis task is to rank nodes based on their relevance to overlapping
communities of shared metadata attributes (eg the interests of social network users). To …

Graph filters are an emerging paradigm that systematizes information propagation in graphs
as transformation of prior node values, called graph signals, to posterior scores. In this work …

An emerging problem in network analysis is ranking network nodes based on their
relevance to metadata groups that share attributes of interest, for example in the context of …

Personalized PageRank (PPR) is a popular scheme for scoring the relevance of network
nodes to a set of seed ones through a random walk with restart process. Calculating the …

In this deliverable we explore state-of-the art and new approaches for mining
heterogeneous social graphs in decentralized time-evolving systems. Such approaches can …

A problem that frequently occurs when mining complex networks is selecting algorithms with
which to rank the relevance of nodes to metadata groups characterized by a small number of …

Graph filters are an emerging paradigm that systematizes information propagation in graphs
as transformation of prior node values, called graph signals, to posterior scores. In this work …