JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (3): 1-13.doi: 10.6040/j.issn.1671-9352.7.2023.787

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Heterogeneous network representation learning based on metapath attribute fusion

Jinghong WANG1,2,3(),Zhibing WU1,Peng HUANG1,Jiateng YANG1,Bi LI4,*()   

  1. 1. School of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang 050024, Hebei, China
    2. Hebei Key Laboratory of Network and Information Security (Hebei Normal University), Shijiazhuang 050024, Hebei, China
    3. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Security (Hebei Normal University), Shijiazhuang 050024, Hebei, China
    4. School of Business, Hebei Normal University, Shijiazhuang 050024, Hebei, China
  • Received:2023-04-29 Online:2024-03-20 Published:2024-03-06
  • Contact: Bi LI E-mail:wangjinghong@126.com;libilb@263.net

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Abstract:

Focusing on the research on representation learning of information networks, a metapath attribute fusion graph neural network (MAFGNN) based on metapath information fusion is proposed, which is to integrate the neighbor information of the target node, including the metapath information, into the node before introducing the metapath in the heterogeneous network to achieve the fusion of target node and neighbor information. This method first converts the attribute features of different types of nodes into dimensions to facilitate subsequent fusion operations. The fusion operation of target node information is completed by calculating the weight values of target nodes and neighbor nodes. Then target nodes are fused according to specific metapaths, and finally different semantic information is fused between different metapaths. Experiments on multiple heterogeneous information datasets show that the MAFGNN model has the best performance and more accurate prediction results than the most advanced benchmark experiments in dealing with heterogeneous network node embedding.

Key words: metapath, heterogeneous information network, heterogeneous graph embedding, information fusion, attention mechanism

CLC Number: 

  • TP181

Fig.1

Heterogeneous information networks and metapaths"

Table 1

This article uses various symbols to summarize"

符号 解释
G G=(V, E)异质图信息网络
V、G 异质网络中的节点集、边集
P 元路径
GP 基于元路径P的子图
h、h′ 初始节点特征向量、转换后节点特征向量
p 基于元路径P的路径实例
W 特定节点类型转换矩阵
ei, jP 基于元路径P的节点对(i, j)重要性系数
hnodeP 元路径P的节点级注意力向量
Zv 语义层级节点嵌入向量

Fig.2

Schematic diagram of MAFGNN model"

Fig.3

Network structures based on different metapaths"

Fig.4

Neighbor nodes based on different metapath target nodes"

Table 2

MAFGNN algorithm process"

算法1 基于元路径与属性融合的异质网络表示学习
输入：异质信息网络G={V, E}，节点特征h=(h1, h2, …, hN)，元路径集合{P1, P2, …, Pn}，节点类型A={A1, A2, …, A|A|};
输出：节点最终表示Z。
1for v in V do //对所有目标节点进行操作。
2通过公式(1)将目标节点和目标节点的邻居节点进行特征维度转换得到转换后的向量表示h′v、h′u;
3通过公式(2)和(3)计算转换后的目标节点和邻居节点之间的权重系数并得到输出结果：$\boldsymbol{h}=a_{v, u} \boldsymbol{h}_v^{\prime}=a_{v, u}\left(\boldsymbol{h}_1, \boldsymbol{h}_2, \cdots, \boldsymbol{h}_N\right)$;
4end for//终止循环。
5for {P1, P2, …, Pn} in P do
6  for v in V do
7    通过公式(4)计算基于元路径Pi的邻居节点重要性Φi, jP;
8    通过公式(5)目标节点基于元路径的节点嵌入hnodeP;
9    if注意力头数K>1 do
10      公式(7)计算嵌入方式
11  end for//终止循环
12end for//终止循环
13通过公式(8)得到每一条元路径的重要性ψP;
14通过公式(9)得到最终的目标节点表示向量Zv;
15return Zv∈V //返回目标节点向量。

Table 3

Experimental dataset required"

数据集 节点类型 节点数量 边关系 边关系数量 元路径
DBLP A 4 057 A—P
P—T
P—V		 19 654
85 810
14 328		 APA
APCPA
APTPA
P 14 328
T 7 723
V 20
IMDB M 4 278 M—D
M—A		 4 278
12 828		 MAM
MDM
D 2 081
A 5 257
ACM A 5 912 P—A
P—S		 9 936
3 025		 PAP
PSP
P 3 025
S 57

Table 4

Node Classification Experiment Result"

Dataset Metrics DeepWalk metapath2vec GCN GAT HERec HAN MAFGNN
DBLP Macro-F1 84.81 91.89 92.38 91.73 92.34 93.80 95.81
Micro-F186.2692.8093.0992.5593.2793.9996.00
IMDB Macro-F1 50.35 45.15 51.81 52.99 47.64 55.54 56.76
Micro-F154.3348.8154.6156.9750.9957.6059.20
ACM Macro-F1 57.38 53.99 61.89 66.39 73.92 77.32 84.83
Micro-F157.5754.3161.5171.5773.8478.2385.77

Table 5

Node clustering experiment results"

Dataset Metrics DeepWalk metapath2vec GCN GAT HERec HAN MAFGNN
DBLP NMI 76.53 74.30 75.01 71.50 76.73 81.98 88.21
ARI81.3578.5080.4977.2680.9887.3792.41
IMDB NMI 1.45 1.20 5.45 8.45 5.45 20.94 25.78
ARI2.141.704.407.464.4023.7029.52
ACM NMI 41.61 21.22 40.44 56.26 40.70 59.17 68.61
ARI35.1021.0029.5953.6937.1359.4871.39

Fig.5

Node embedding experiment results"

Fig.6

Number of training rounds and Loss for DBLP dataset"

Fig.7

DBLP dataset training rounds and Micro-F1"

Fig.8

Attention headcount experiment"

Fig.9

Semantic level attention vector dimension experiment"

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