Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 441-447.DOI: 10.11772/j.issn.1001-9081.2019081529
• CCF NDBC 2019 • Previous Articles Next Articles
Jingfeng GUO1,2, Hui DONG1,2(), Tingwei ZHANG1,2, Xiao CHEN3
Received:
2019-08-12
Revised:
2019-09-10
Accepted:
2019-10-24
Online:
2019-11-04
Published:
2020-02-10
Contact:
Hui DONG
About author:
GUO Jingfeng, born in 1962, Ph. D., professor. His research interests include database, data mining, social network analysis.Supported by:
通讯作者:
董慧
作者简介:
郭景峰(1962—),男,黑龙江哈尔滨人,教授 ,博士,CCF会员,主要研究方向:数据库、数据挖掘、社会网络分析基金资助:
CLC Number:
Jingfeng GUO, Hui DONG, Tingwei ZHANG, Xiao CHEN. Representation learning for topic-attention network[J]. Journal of Computer Applications, 2020, 40(2): 441-447.
郭景峰, 董慧, 张庭玮, 陈晓. 主题关注网络的表示学习[J]. 《计算机应用》唯一官方网站, 2020, 40(2): 441-447.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019081529
节点对 | (u1,u4) | (u5,t3) | (t3,u5) |
---|---|---|---|
S | 1 | 1 | 1 |
∣CNT(ui,uj)1∣ | 0 | 0 | 0 |
∣CNU(ui,uj)1∣ | 2 | 2 | 0 |
∣CNT(ui,uj)1∩2∣ | 1 | 0 | 0 |
∣CNT(ui,uj)2∩1∣ | 1 | 0 | 0 |
∣CNT(ui,uj)2∣ | 1 | 0 | 0 |
Tab. 1 Number of neighbors between node pairs
节点对 | (u1,u4) | (u5,t3) | (t3,u5) |
---|---|---|---|
S | 1 | 1 | 1 |
∣CNT(ui,uj)1∣ | 0 | 0 | 0 |
∣CNU(ui,uj)1∣ | 2 | 2 | 0 |
∣CNT(ui,uj)1∩2∣ | 1 | 0 | 0 |
∣CNT(ui,uj)2∩1∣ | 1 | 0 | 0 |
∣CNT(ui,uj)2∣ | 1 | 0 | 0 |
vcurrent | u2 | u3 | u4 | u5 | u6 | t1 | t2 | t3 |
---|---|---|---|---|---|---|---|---|
u1 | 0.19 | 0.08 | 0.18 | 0.05 | 0.06 | 0.12 | 0.23 | 0.10 |
t3 | 0.00 | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 |
Tab. 2 Transition probability with node u1 and t3as vcurrent
vcurrent | u2 | u3 | u4 | u5 | u6 | t1 | t2 | t3 |
---|---|---|---|---|---|---|---|---|
u1 | 0.19 | 0.08 | 0.18 | 0.05 | 0.06 | 0.12 | 0.23 | 0.10 |
t3 | 0.00 | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 |
算法 | 向量维度d | 算法 | 向量维度d |
---|---|---|---|
Deepwalk | 64 | PTE | 100 |
node2vec | 128 | metapath2vec | 100 |
MPNE | 64 | TANE | 64 |
Tab. 3 Vector dimension settings of different algorithms
算法 | 向量维度d | 算法 | 向量维度d |
---|---|---|---|
Deepwalk | 64 | PTE | 100 |
node2vec | 128 | metapath2vec | 100 |
MPNE | 64 | TANE | 64 |
vnext | P(vnext∣vcurrent) | vnext | P(vnext∣vcurrent) |
---|---|---|---|
u5 | 0.029 847 | u9 | 0.025 764 |
u6 | 0.029 360 | u10 | 0.005 381 |
u7 | 0.029 360 | u11 | 0.029 847 |
u8 | 0.034 477 | u12 | 0.020 588 |
Tab. 4 Transition probability between u1 and nodes in the range of bold line segment in Fig. 5(a)
vnext | P(vnext∣vcurrent) | vnext | P(vnext∣vcurrent) |
---|---|---|---|
u5 | 0.029 847 | u9 | 0.025 764 |
u6 | 0.029 360 | u10 | 0.005 381 |
u7 | 0.029 360 | u11 | 0.029 847 |
u8 | 0.034 477 | u12 | 0.020 588 |
算法 | K | ||||||||
---|---|---|---|---|---|---|---|---|---|
5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
DeepWalk | 0.546 5 | 0.552 1 | 0.545 7 | 0.530 1 | 0.551 2 | 0.565 4 | 0.578 5 | 0.482 1 | 0.574 4 |
Node2vec | 0.552 4 | 0.582 5 | 0.594 3 | 0.563 2 | 0.605 8 | 0.635 6 | 0.622 4 | 0.627 9 | 0.626 3 |
MPNE | 0.499 5 | 0.554 8 | 0.572 1 | 0.572 3 | 0.558 8 | 0.584 6 | 0.629 5 | 0.617 7 | 0.634 5 |
PTE | 0.551 7 | 0.586 2 | 0.593 3 | 0.587 6 | 0.602 5 | 0.639 6 | 0.628 7 | 0.639 5 | 0.645 8 |
matepath2vec | 0.558 3 | 0.573 6 | 0.589 3 | 0.598 1 | 0.599 4 | 0.640 3 | 0.631 2 | 0.643 1 | 0.650 2 |
TANE | 0.5665 | 0.5976 | 0.5970 | 0.612 7 | 0.6092 | 0.6435 | 0.6325 | 0.6568 | 0.6998 |
Tab. 5 Comparison of Q values on Douban dataset by different algorithms
算法 | K | ||||||||
---|---|---|---|---|---|---|---|---|---|
5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
DeepWalk | 0.546 5 | 0.552 1 | 0.545 7 | 0.530 1 | 0.551 2 | 0.565 4 | 0.578 5 | 0.482 1 | 0.574 4 |
Node2vec | 0.552 4 | 0.582 5 | 0.594 3 | 0.563 2 | 0.605 8 | 0.635 6 | 0.622 4 | 0.627 9 | 0.626 3 |
MPNE | 0.499 5 | 0.554 8 | 0.572 1 | 0.572 3 | 0.558 8 | 0.584 6 | 0.629 5 | 0.617 7 | 0.634 5 |
PTE | 0.551 7 | 0.586 2 | 0.593 3 | 0.587 6 | 0.602 5 | 0.639 6 | 0.628 7 | 0.639 5 | 0.645 8 |
matepath2vec | 0.558 3 | 0.573 6 | 0.589 3 | 0.598 1 | 0.599 4 | 0.640 3 | 0.631 2 | 0.643 1 | 0.650 2 |
TANE | 0.5665 | 0.5976 | 0.5970 | 0.612 7 | 0.6092 | 0.6435 | 0.6325 | 0.6568 | 0.6998 |
实验组序号m | α | β | γ1 | γ2 | δ | χ1 | χ2 | ε | θ | Qm |
---|---|---|---|---|---|---|---|---|---|---|
1 | 0.75 | 0.25 | 0.00 | 0.00 | 0.00 | 0 | 1 | 0.7 | 0.3 | 0.63 |
2 | 0.40 | 0.30 | 0.10 | 0.10 | 0.10 | 1/3 | 2/3 | 0.7 | 0.3 | 0.70 |
3 | 0.25 | 0.25 | 1/6 | 1/6 | 1/6 | 1/2 | 1/2 | 0.7 | 0.3 | 0.52 |
4 | 0.10 | 0.30 | 0.20 | 0.20 | 0.20 | 2/3 | 1/3 | 0.7 | 0.3 | 0.37 |
5 | 0.00 | 0.25 | 0.25 | 0.25 | 0.25 | 1 | 0 | 0.7 | 0.3 | 0.16 |
Tab. 6 Parameters analysis of transition probability model
实验组序号m | α | β | γ1 | γ2 | δ | χ1 | χ2 | ε | θ | Qm |
---|---|---|---|---|---|---|---|---|---|---|
1 | 0.75 | 0.25 | 0.00 | 0.00 | 0.00 | 0 | 1 | 0.7 | 0.3 | 0.63 |
2 | 0.40 | 0.30 | 0.10 | 0.10 | 0.10 | 1/3 | 2/3 | 0.7 | 0.3 | 0.70 |
3 | 0.25 | 0.25 | 1/6 | 1/6 | 1/6 | 1/2 | 1/2 | 0.7 | 0.3 | 0.52 |
4 | 0.10 | 0.30 | 0.20 | 0.20 | 0.20 | 2/3 | 1/3 | 0.7 | 0.3 | 0.37 |
5 | 0.00 | 0.25 | 0.25 | 0.25 | 0.25 | 1 | 0 | 0.7 | 0.3 | 0.16 |
数据集 | α | β | γ1 | γ2 | δ | χ1 | χ2 | ε | θ |
---|---|---|---|---|---|---|---|---|---|
Karate | 0.35 | 0.25 | 0.15 | 0.15 | 0.1 | 2/3 | 1/3 | 0.7 | 0.3 |
豆瓣 | 0.40 | 0.30 | 0.10 | 0.10 | 0.1 | 2/3 | 1/3 | 0.7 | 0.3 |
Tab. 7 Parameters setting of transition probability model
数据集 | α | β | γ1 | γ2 | δ | χ1 | χ2 | ε | θ |
---|---|---|---|---|---|---|---|---|---|
Karate | 0.35 | 0.25 | 0.15 | 0.15 | 0.1 | 2/3 | 1/3 | 0.7 | 0.3 |
豆瓣 | 0.40 | 0.30 | 0.10 | 0.10 | 0.1 | 2/3 | 1/3 | 0.7 | 0.3 |
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