Accelerated Attributed Network Embedding

By Xiao Huang, Jundong Li, Xia Hu. SDM, 2017

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0efb659b15737c76a2fc50010a694123f6c45f64

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Tat-seng Chua
3. Xia Hu
4. Philip S. Yu
5. Peng Cui
6. Huan Liu
7. Xiao Wang
8. Wenwu Zhu

In Molecular Biology

1. Rik Sarkar
2. Benedek Rozemberczki
3. Carl Allen
4. Chuan Zhou
5. Xiangnan He
6. Xia Hu
7. Huan Liu
8. Tat-seng Chua

In Statistical and Nonlinear Physics

1. Peng Cui
2. Wenwu Zhu
3. Xiao Wang
4. Palash Goyal
5. Xia Hu
6. Huan Liu
7. Benedek Rozemberczki
8. Jian Pei

Citations in the corpus

• A Comprehensive Survey on Community Detection With Deep Learning (2021, methodology): “For attributed graphs, community detection via a GCN relies on both structural information and attributed features [90], where neighboring nodes and the nodes with similar features are likely to cluster in the same community.”
• Large-Scale Heterogeneous Feature Embedding (2019, methodology): “• AANE (Huang, Li, and Hu 2017a): It is the state-of-the-art embedding method for networks with node attributes.”
• A Survey on Network Embedding (2017, methodology): “LANE (Huang, Li, and Hu 2017) is also proposed to incorporate the label information into the attributed network embedding.”
• Utilizing graph machine learning within drug discovery and development (2021, methodology): “Another approach is to jointly factorise both data sources, for instance, introducing a kernel defined on the feature matrix [80, 81].”
• Multi-scale Attributed Node Embedding (2019, methodology): “Many other methods exist, but do not consider the attributes of higher-order neighbourhoods [6, 7].”
• Embedding Networks with Edge Attributes (2018, background): “Recently, some approaches have been proposed to incorporate node attributes in learning the representation [6, 12, 13].”
• Representation Learning for Attributed Multiplex Heterogeneous Network (2019, background): “Single Single Attributed LANE [16] AANE [15] SNE [20] DANE [9] ANRL [44]”
• Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017, background): “These types of networks are called attributed networks [Huang et al., 2017a; 2017b].”
• Attributed Social Network Embedding (2017, background): “possibility of integrating contents to learn informative representations [33].”
• Under review as a conference paper at ICLR 2019 MILE : A Multi-Level Framework for Scalable Graph Embedding (2018, unknown): “On the other hand, there are very few efforts, focusing on the scalability of network embedding (Yang et al., 2017; Huang et al., 2017).”
• AliGraph: A Comprehensive Graph Neural Network Platform (2019, methodology): “In our system, we abstract two kinds of operators, namely AGGREGATE and COMBINE [30, 7, 24, 22].”