‏‏‎ ‎ 2020

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.

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.

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

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

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

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 Social Networks Using Shortest Paths

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

In this paper, we propose to use the concept of shortest path for sampling social networks.

On Random Walk Based Graph Sampling

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm.

Leveraging History for Faster Sampling of Online Social Networks

‏‏‎ ‎ 2020 benedekrozemberczki/littleballoffur

Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).