17 citations · 61 across the 14 of their papers we have counts for
3 papers · 1 filter
Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings
Benedek Rozemberczki, Rik Sarkar
Proximity preserving and structural role-based node embeddings have become a prime workhorse of applied graph mining. Novel node embedding techniques are often tested on a restrict…
Little Ball of Fur: A Python Library for Graph Sampling
Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our go…
GEMSEC: Graph Embedding with Self Clustering
Benedek Rozemberczki, Ryan Davies, Rik Sarkar +1
Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering o…