基于高阶相似性的属性网络表示学习

doi:10.11959/j.issn.1000-0801.2020309

Abstract

Abstract:

Due to the lack of deep-level information mining and utilization in the existing network representation learning methods,the potential pattern structure similarity was proposed by further exploring the potential information in the network.The similarity score between network structures was defined to measure the similarity between various structures so that nodes could cross irrelevant vertices to obtain high-order similarities on the global structure.In order to achieve the best effect,deep learning was used to fuse multiple information sources to participate in training together to make up for the deficiency of random walks.In the experiment,Lap,DeepWalk,TADW,SDNE and CANE were selected as comparison methods,and three real-world networks were used as data sets to verify the validity of the model,and experiments of node classification and link reconstruction are carried out.In the node classification,the average performance is improved by 1.7 percentage points for different datasets and training proportions.In the link reconstruction experiment,only half the dimension is needed to achieve better performance.Finally,the performance improvement of the model under different network depths was discussed.By increasing the depth of the model,the average performance of node classification increased by 1.1 percentage points.

Key words: network representation learning, graph embedding, attribute network, structure information

CLC Number:

TP311

Shaoqing WU,Yihong DONG,Xiong WANG,Yan CAO,Yu XIN. Learning attribute network algorithm based on high-order similarity[J]. Telecommunications Science, 2020, 36(12): 20-32.

Figures/Tables 12

References 13

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方法	30%	40%	50%	60%	70%	80%	90%
Lap	29.66%	30.54%	31.67%	33.87%	36.29%	39.53%	42.71%
DeepWalk	79.58%	80.88%	81.54%	82.12%	82.90%	82.83%	83.88%
SDNE	59.51%	61.74%	63.39%	64.36%	65.05%	65.61%	65.55%
TADW	53.49%	56.17%	57.92%	59.47%	60.29%	60.86%	61.72%
CANE	57.28%	60.25%	60.89%	63.27%	64.08%	64.65%	65.24%
LANAHS_A	81.02%	81.27%	82.26%	83.02%	83.71%	84.12%	84.31%
LANAHS	82.15%	82.84%	83.57%	84.21%	84.62%	85.06%	85.73%

方法	30%	40%	50%	60%	70%	80%	90%
Lap	51.61%	52.86%	55.11%	56.64%	58.27%	59.71%	59.59%
DeepWalk	82.56%	82.71%	82.64%	82.65%	82.79%	83.17%	83.25%
SDNE	53.08%	53.10%	53.18%	53.26%	53.71%	54.08%	54.15%
TADW	82.50%	82.71%	82.91%	83.29%	83.29%	84.04%	84.04%
CANE	82.61%	82.82%	83.08%	83.12%	83.13%	83.24%	83.56%
LANAHS_A	83.01%	83.24%	83.59%	83.62%	83.73%	84.25%	84.25%
LANAHS	83.75%	84.75%	84.89%	85.26%	85.28%	85.43%	85.72%

方法	Lap	DeepWalk	SDNE	TADW	CANE	LANAHS_A	LANAHS
16维	0.959 2	0.995 2	0.652 4	0.569 5	0.602 5	0.985 4	0.996 3
32维	0.973 1	0.997 3	0.662 0	0.599 6	0.637 1	0.994 1	0.998 5
48维	0.978 6	0.998 1	0.678 9	0.626 0	0.662 1	0.998 4	0.999 0
64维	0.981 6	0.998 4	0.679 4	0.630 6	0.673 6	0.999 0	0.999 0
80维	0.983 6	0.998 5	0.718 9	0.661 6	0.692 1	0.999 0	0.999 0
96维	0.985 8	0.998 5	0.738 1	0.661 8	0.708 2	0.998 9	0.999 0
112维	0.986 9	0.998 5	0.773 6	0.669 5	0.725 4	0.998 8	0.999 0
128维	0.988 8	0.998 5	0.887 7	0.679 4	0.736 5	0.998 8	0.999 0

方法	Lap	DeepWalk	SDNE	TADW	CANE	LANAHS_A	LANAHS
16维	0.910 9	0.994 8	0.519 6	0.918 7	0.905 4	0.982 5	0.996 9
32维	0.930 1	0.997 0	0.523 4	0.955 3	0.952 3	0.983 5	0.998 1
48维	0.952 1	0.997 8	0.524 2	0.966 6	0.957 4	0.983 7	0.999 2
64维	0.958 6	0.998 2	0.526 7	0.973 0	0.978 7	0.985 4	0.999 2
80维	0.965 9	0.998 5	0.531 6	0.976 4	0.979 2	0.993 9	0.999 3
96维	0.975 3	0.998 7	0.533 6	0.978 7	0.985 8	0.994 0	0.999 3
112维	0.976 8	0.998 8	0.534 1	0.980 5	0.985 9	0.996 5	0.999 3
128维	0.978 4	0.998 9	0.539 3	0.981 9	0.986 3	0.996 7	0.999 3

方法	Lap	DeepWalk	SDNE	TADW	CANE	LANAHS_A	LANAHS
16维	0.791 4	0.909 8	0.520 2	0.799 8	0.838 6	0.912 3	0.918 2
32维	0.811 6	0.927 0	0.570 6	0.828 1	0.857 9	0.917 5	0.940 1
48维	0.852 9	0.935 1	0.537 6	0.838 0	0.862 4	0.923 4	0.949 7
64维	0.866 3	0.939 8	0.571 5	0.848 2	0.871 2	0.934 9	0.953 5
80维	0.876 5	0.942 7	0.611 7	0.855 5	0.884 1	0.942 1	0.955 7
96维	0.883 8	0.944 7	0.567 3	0.862 4	0.889 7	0.945 6	0.955 4
112维	0.890 6	0.945 9	0.608 1	0.866 3	0.890 1	0.948 0	0.955 1
128维	0.895 8	0.946 9	0.561 1	0.871 7	0.894 2	0.947 3	0.9543

Learning attribute network algorithm based on high-order similarity

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Figures/Tables 12

References 13

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信息比例	Cora	DBLP
1:1:1	83.65%	84.72%
2:2:1	83.05%	85.26%
2:1:1	84.21%	84.21%
2:1:2	83.37%	83.12%

层数	Cora	DBLP
无隐藏层	82.65%	84.02%
1层	83.57%	84.89%
2层	84.24%	85.45%
3层	84.31%	86.32%
4层	84.31%	86.15%

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