BiNE（2023.2.3）-已初步跑通，博客已记录理论.zip

# BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: > BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiangnan He & Aoying Zhou which has been accepted by **SIGIR2018**. `Note`: Any problems, you can contact me at [leihuichen@gmail.com](mailto:leihuichen@gmail.com). Through email, you will get my rapid response. ## Environment settings - python==2.7.11 - numpy==1.13.3 - sklearn==0.17.1 - networkx==1.11 - datasketch==1.2.5 - scipy==0.17.0 - six==1.10.0 ## Basic Usage **Main Parameters:** ``` Input graph path. Defult is '../data/rating_train.dat' (--train-data) Test dataset path. Default is '../data/rating_test.dat' (--test-data) Name of model. Default is 'default' (--model-name) Number of dimensions. Default is 128 (--d) Number of negative samples. Default is 4 (--ns) Size of window. Default is 5 (--ws) Trade-off parameter $\alpha$. Default is 0.01 (--alpha) Trade-off parameter $\beta$. Default is 0.01 (--beta) Trade-off parameter $\gamma$. Default is 0.1 (--gamma) Learning rate $\lambda$. Default is 0.01 (--lam) Maximal iterations. Default is 50 (--max-iters) Maximal walks per vertex. Default is 32 (--maxT) Minimal walks per vertex. Default is 1 (--minT) Walk stopping probability. Default is 0.15 (--p) Calculate the recommendation metrics. Default is 0 (--rec) Calculate the link prediction. Default is 0 (--lip) File of training data for LR. Default is '../data/wiki/case_train.dat' (--case-train) File of testing data for LR. Default is '../data/wiki/case_test.dat' (--case-test) File of embedding vectors of U. Default is '../data/vectors_u.dat' (--vectors-u) File of embedding vectors of V. Default is '../data/vectors_v.dat' (--vectors-v) For large bipartite, 1 do not generate homogeneous graph file; 2 do not generate homogeneous graph. Default is 0 (--large) Mertics of centrality. Default is 'hits', options: 'hits' and 'degree_centrality' (--mode) ``` **Usage** We provide two processed dataset: - DBLP (for recommendation). It contains: - A training dataset ./data/dblp/rating_train.dat - A testing dataset ./data/dblp/rating_test.dat - Wikipedia (for link prediction). It contains: - A training dataset ./data/wiki/rating_train.dat - A testing dataset ./data/wiki/rating_test.dat - Each line is a instance: userID (begin with 'u')\titemID (begin with 'i') \t weight\n For example: u0\ti0\t1 Please run the './model/train.py' ``` cd model python train.py --train-data ../data/dblp/rating_train.dat --test-data ../data/dblp/rating_test.dat --lam 0.025 --max-iter 100 --model-name dblp --rec 1 --large 2 --vectors-u ../data/dblp/vectors_u.dat --vectors-v ../data/dblp/vectors_v.dat ``` The embedding vectors of nodes are saved in file '/model-name/vectors_u.dat' and '/model-name/vectors_v.dat', respectively. ## Example ### Recommendation **Run** ``` cd model python train.py --train-data ../data/dblp/rating_train.dat --test-data ../data/dblp/rating_test.dat --lam 0.025 --max-iter 100 --model-name dblp --rec 1 --large 2 --vectors-u ../data/dblp/vectors_u.dat --vectors-v ../data/dblp/vectors_v.dat ``` **Output** (training process) ``` ======== experiment settings ========= alpha : 0.0100, beta : 0.0100, gamma : 0.1000, lam : 0.0250, p : 0.1500, ws : 5, ns : 4, maxT : 32, minT : 1, max_iter : 100 ========== processing data =========== constructing graph.... number of nodes: 6001 walking... walking...ok number of nodes: 1177 walking... walking...ok getting context and negative samples.... negative samples is ok..... context... context...ok context... context...ok ============== training ============== [*************************************************************************************************** ]100.00% ``` **Output** (testing process) ``` ============== testing =============== recommendation metrics: F1 : 0.1132, MAP : 0.2041, MRR : 0.3331, NDCG : 0.2609 ``` ### Link Prediction **Run** ``` cd model python train.py --train-data ../data/wiki/rating_train.dat --test-data ../data/wiki/rating_test.dat --lam 0.01 --max-iter 100 --model-name wiki --lip 1 --large 2 --gamma 1 --vectors-u ../data/wiki/vectors_u.dat --vectors-v ../data/wiki/vectors_v.dat --case-train ../data/wiki/case_train.dat --case-test ../data/wiki/case_test.dat ``` **Output** (training process) ``` ======== experiment settings ========= alpha : 0.0100, beta : 0.0100, gamma : 1.0000, lam : 0.0100, p : 0.1500, ws : 5, ns : 4, maxT : 32, minT : 1, max_iter : 100, d : 128 ========== processing data =========== constructing graph.... number of nodes: 15000 walking... walking...ok number of nodes: 2529 walking... walking...ok getting context and negative samples.... negative samples is ok..... context... context...ok context... context...ok ============== training ============== [*************************************************************************************************** ]100.00% ``` **Output** (testing process) ``` ============== testing =============== link prediction metrics: AUC_ROC : 0.9468, AUC_PR : 0.9614 ```