Search Results for author: Renming Liu

Found 4 papers, 3 papers with code

Towards a Taxonomy of Graph Learning Datasets

no code implementations27 Oct 2021 Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.

Graph Learning

Accurately Modeling Biased Random Walks on Weighted Graphs Using $\textit{Node2vec+}$

1 code implementation15 Sep 2021 Renming Liu, Matthew Hirn, Arjun Krishnan

$\textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph.

PecanPy: A parallelized, efficient, and accelerated node2vec in Python

1 code implementation23 Jul 2020 Renming Liu, Arjun Krishnan

Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks.

Supervised learning is an accurate method for network-based gene classification

1 code implementation1 Jun 2020 Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson, Arjun Krishnan

Results: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes.

General Classification

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