Deep network embedding for graph representation learning in signed networks

This repository contains the author's implementation in Matlab for the paper "Deep network embedding for graph representation learning in signed networks".

DNE-SBP Model Descriptions

Input

Load ".mat" file and get an input matrix "Gwl_ud", i.e., the signed adjacency matrix of a network.

Hyperparameters

1. beta: ratio of penalty on reconstruction errors of observed connections over that of unobserved connections

2. r= #positive edges / #negative edges;

   ratio of penalty for reconstruction errors of negative links over that of positive links;

   ratio of weight of pairwise constraints on negatively connected nodes over that of positively connected nodes

3. alfa1: weight of pairwise constraints at 1-st layer of SAE

   alfa2: weight of pairwise constraints at deep layers of SAE

Output

Low-dimensional node vector representations learned by DNE-SBP are stored in the variable: "rep"

Application 1: Link sign prediction

The function DNESBP_LP() in file “DNESBP_LP.m" can generate low-dimensional node vector representations for link sign prediction Test examples:

1. In MATLAB, run “DNESBP_LP_wiki.m”, “DNESBP_LP_slashdot.m”, “DNESBP_LP_epinions.m” for example link sign prediction results on Wiki, Slashdot and Epinions datasets, respectively.

2. Use variable “trp” to assign different training percentages. For example, "trp=0.2" indicates training percentage fixed as 20%. "trp=[0.2,0.4,0.6,0.8]" indicates training percentage can be varied among 20%, 40%, 60% and 80%.

3. The AUC and AP averaged over 5 random splits are stored in the variables: “avgAUC” and “avgAPN”, where each row corresponds to a type of edge feature, i.e., "L1", "L2", "Had", and "Avg"; each column corresponds to a specific training percentage, e.g., " 20%", "40%", "60%" or "80%".

Application 2: Signed network community detection

The function DNESBP_CD() in file “DNESBP_CD.m" can generate low-dimensional node vector representations for signed network community detection Test examples:

1. In MATLAB, run files “DNESBP_CD_wiki.m”, “DNESBP_CD_slashdot.m”, “DNESBP_CD_epinions.m” for example community detection results on Wiki, Slashdot and Epinions datasets, respectively.

2. Use variable “numCluster” to assign different numbers of clusters. For example, "numCluster=2:10" indicates the number of clusters can be varied between 2 and 10.

3. The error rates of signed network clustering are stored in the variable: “errorAllK”, where each k-th column corresponds to the error rate given a specific number of k clusters.

Please cite our paper as

X. Shen and F.-L. Chung, "Deep network embedding for graph representation learning in signed networks," IEEE Transactions on Cybernetics, vol. 50, no. 4, pp. 1556-1568, 2020.