Vertex-reinforced Random Walk for Network Embedding

Xiao, Wenyi; Zhao, Huan; Zheng, Vincent W.; Song, Yangqiu

doi:10.1137/1.9781611976236.67

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, Struct2Vec [22] constructs the multilayers graph for different hierarchical levels and follows node2vec to learn the representation for each layer. DRRW [23] analyzes the convergence of random path and proposes an exploration score to guide the path toward less-visited nodes for better distribution learning. Extended studies further aim at learning node embeddings in attributed networks, in which ANRL [24], RWR-GAE [25], and ARWR-GE [26] are random walk-based approaches that also incorporate the Skip-gram model as a component for the graph structure preservation.…”

Section: Related Workmentioning

confidence: 99%

Toward an Adaptive Skip-Gram Model for Network Representation Learning

Hsieh

2022

IEEE Access

View full text Add to dashboard Cite

The random walk process on network data is a widely-used approach for network representation learning. However, we argue that the sampling of node sequences and the subsampling for the Skip-gram's contexts have two drawbacks. One is less possible to precisely find the most correlated context nodes for every central node with only uniform graph search. The other is not easily controlled due to the expensive cost of hyperparameter tuning. Such two drawbacks lead to higher training cost and lower accuracy due to abundant and irrelevant samples. To solve these problems, we compute the adaptive probability of random walk based on Personalized PageRank (PPR), and propose an Adaptive SKip-gram (ASK) model without using complicated sampling process and negative sampling. We utilize k-most important neighbors for positive samples selection, and attach their corresponding PPR probability into the objective function. Based on benchmark datasets with three citation networks and three social networks, we demonstrate the improvement of our ASK model for network representation learning in tasks of link prediction, node classification, and embedding visualization. The results achieve more effective performance and efficient learning time.

show abstract

Section: Related Workmentioning

confidence: 99%

Toward an Adaptive Skip-Gram Model for Network Representation Learning

Hsieh

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

A Survey on Graph Representation Learning Methods

Khoshraftar,

2024

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

Graphs representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques have been proposed for generating effective graph representation vectors, which generally fall into two categories: traditional graph embedding methods and graph neural nets (GNN) based methods. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, while a dynamic graph evolves over time and its nodes and edges can be added or deleted from the graph. In this survey, we review the graph embedding methods in both traditional and GNN-based categories for both static and dynamic graphs and include the recent papers published until the time of submission. In addition, we summarize a number of limitations of GNNs and the proposed solutions to these limitations. Such a summary has not been provided in previous surveys. Finally, we explore some open and ongoing research directions for future work.

show abstract

Vertex-reinforced Random Walk for Network Embedding

Cited by 2 publications

References 30 publications

Toward an Adaptive Skip-Gram Model for Network Representation Learning

Toward an Adaptive Skip-Gram Model for Network Representation Learning

A Survey on Graph Representation Learning Methods

Contact Info

Product

Resources

About