‏‏‎ ‎ 2020

Fast Network Embedding Enhancement via High Order Proximity Approximation

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.

DIMENSIONALITY REDUCTION LINK PREDICTION MULTI-LABEL CLASSIFICATION NETWORK EMBEDDING

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data.

DIMENSIONALITY REDUCTION

NetLSD: Hearing the Shape of a Graph

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation.

NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

Embeddings have become a key paradigm to learn graph represen-tations and facilitate downstream graph analysis tasks.

Asymmetric Transitivity Preserving Graph Embedding

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.

GRAPH EMBEDDING LINK PREDICTION

Network Sampling: From Static to Streaming Graphs

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Network sampling is integral to the analysis of social, information, and biological networks.

Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph.

Reducing Large Internet Topologies for Faster Simulations

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

In this paper, we develop methods to “sample” a small realistic graph from a large real network.

Sampling Community Structure

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

We propose a novel method, based on concepts from expander graphs, to sample communities in networks.

COMMUNITY DETECTION RELATIONAL REASONING

Metropolis Algorithms for Representative Subgraph Sampling

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.