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GraphEmbedding-master/LICENSE 0 → 100644
View file @ 2842c55e
MIT License
Copyright (c) 2019 Weichen Shen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
\ No newline at end of file
GraphEmbedding-master/README.md 0 → 100644
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# GraphEmbedding
# Method
| Model | Paper | Note |
| :-------: | :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------ |
| DeepWalk | [KDD 2014][DeepWalk: Online Learning of Social Representations](http://www.perozzi.net/publications/14_kdd_deepwalk.pdf) | [【Graph Embedding】DeepWalk：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56380812) |
| LINE | [WWW 2015][LINE: Large-scale Information Network Embedding](https://arxiv.org/pdf/1503.03578.pdf) | [【Graph Embedding】LINE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56478167) |
| Node2Vec | [KDD 2016][node2vec: Scalable Feature Learning for Networks](https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf) | [【Graph Embedding】Node2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56542707) |
| SDNE | [KDD 2016][Structural Deep Network Embedding](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf) | [【Graph Embedding】SDNE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56637181) |
| Struc2Vec | [KDD 2017][struc2vec: Learning Node Representations from Structural Identity](https://arxiv.org/pdf/1704.03165.pdf) | [【Graph Embedding】Struc2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56733145) |
# How to run examples
1. clone the repo and make sure you have installed `tensorflow` or `tensorflow-gpu` on your local machine.
2. run following commands
```bash
python setup.py install
cd examples
python deepwalk_wiki.py
```
## DisscussionGroup & Related Projects
<html>
<table style="margin-left: 20px; margin-right: auto;">
<tr>
<td>
公众号：<b>浅梦的学习笔记</b><br><br>
<a href="https://github.com/shenweichen/GraphEmbedding">
<img align="center" src="./pics/code.png" />
</a>
</td>
<td>
微信：<b>deepctrbot</b><br><br>
<a href="https://github.com/shenweichen/GraphEmbedding">
<img align="center" src="./pics/deepctrbot.png" />
</a>
</td>
<td>
<ul>
<li><a href="https://github.com/shenweichen/AlgoNotes">AlgoNotes</a></li>
<li><a href="https://github.com/shenweichen/DeepCTR">DeepCTR</a></li>
<li><a href="https://github.com/shenweichen/DeepMatch">DeepMatch</a></li>
<li><a href="https://github.com/shenweichen/DeepCTR-Torch">DeepCTR-Torch</a></li>
</ul>
</td>
</tr>
</table>
</html>
# Usage
The design and implementation follows simple principles(**graph in,embedding out**) as much as possible.
## Input format
we use `networkx`to create graphs.The input of networkx graph is as follows:
`node1 node2 <edge_weight>`
![](./pics/edge_list.png)
## DeepWalk
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])# Read graph
model = DeepWalk(G,walk_length=10,num_walks=80,workers=1)#init model
model.train(window_size=5,iter=3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## LINE
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = LINE(G,embedding_size=128,order='second') #init model,order can be ['first','second','all']
model.train(batch_size=1024,epochs=50,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## Node2Vec
```python
G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])#read graph
model = Node2Vec(G, walk_length = 10, num_walks = 80,p = 0.25, q = 4, workers = 1)#init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## SDNE
```python
G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = SDNE(G,hidden_size=[256,128]) #init model
model.train(batch_size=3000,epochs=40,verbose=2)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
## Struc2Vec
```python
G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph
model = model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model
model.train(window_size = 5, iter = 3)# train model
embeddings = model.get_embeddings()# get embedding vectors
```
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node label
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GraphEmbedding-master/data/wiki/Wiki_category.txt 0 → 100644
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GraphEmbedding-master/data/wiki/Wiki_edgelist.txt 0 → 100644
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GraphEmbedding-master/data/wiki/wiki_labels.txt 0 → 100644
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GraphEmbedding-master/examples/__init__.py 0 → 100644
View file @ 2842c55e
from .models import *
from .deepwalk import DeepWalk
from .node2vec import Node2Vec
from .line import LINE
from .sdne import SDNE
from .struc2vec import Struc2Vec
__all__ = ["DeepWalk", "Node2Vec", "LINE", "SDNE", "Struc2Vec"]
\ No newline at end of file
GraphEmbedding-master/examples/alias.py 0 → 100644
View file @ 2842c55e
import numpy as np
def create_alias_table(area_ratio):
"""
:param area_ratio: sum(area_ratio)=1
:return: accept,alias
"""
l = len(area_ratio)
accept, alias = [0] * l, [0] * l
small, large = [], []
area_ratio_ = np.array(area_ratio) * l
for i, prob in enumerate(area_ratio_):
if prob < 1.0:
small.append(i)
else:
large.append(i)
while small and large:
small_idx, large_idx = small.pop(), large.pop()
accept[small_idx] = area_ratio_[small_idx]
alias[small_idx] = large_idx
area_ratio_[large_idx] = area_ratio_[large_idx] - \
(1 - area_ratio_[small_idx])
if area_ratio_[large_idx] < 1.0:
small.append(large_idx)
else:
large.append(large_idx)
while large:
large_idx = large.pop()
accept[large_idx] = 1
while small:
small_idx = small.pop()
accept[small_idx] = 1
return accept, alias
def alias_sample(accept, alias):
"""
:param accept:
:param alias:
:return: data_train index
"""
N = len(accept)
i = int(np.random.random()*N)
r = np.random.random()
if r < accept[i]:
return i
else:
return alias[i]
GraphEmbedding-master/examples/classify.py 0 → 100644
View file @ 2842c55e
from __future__ import print_function
import numpy
from sklearn.metrics import f1_score, accuracy_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
class TopKRanker(OneVsRestClassifier):
def predict(self, X, top_k_list):
probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))
all_labels = []
for i, k in enumerate(top_k_list):
probs_ = probs[i, :]
labels = self.classes_[probs_.argsort()[-k:]].tolist()
probs_[:] = 0
probs_[labels] = 1
all_labels.append(probs_)
return numpy.asarray(all_labels)
class Classifier(object):
def __init__(self, embeddings, clf):
self.embeddings = embeddings
self.clf = TopKRanker(clf)
self.binarizer = MultiLabelBinarizer(sparse_output=True)
def train(self, X, Y, Y_all):
self.binarizer.fit(Y_all)
X_train = [self.embeddings[x] for x in X]
Y = self.binarizer.transform(Y)
self.clf.fit(X_train, Y)
def evaluate(self, X, Y):
top_k_list = [len(l) for l in Y]
Y_ = self.predict(X, top_k_list)
Y = self.binarizer.transform(Y)
averages = ["micro", "macro", "samples", "weighted"]
results = {}
for average in averages:
results[average] = f1_score(Y, Y_, average=average)
results['acc'] = accuracy_score(Y,Y_)
print('-------------------')
print(results)
return results
print('-------------------')
def predict(self, X, top_k_list):
X_ = numpy.asarray([self.embeddings[x] for x in X])
Y = self.clf.predict(X_, top_k_list=top_k_list)
return Y
def split_train_evaluate(self, X, Y, train_precent, seed=0):
state = numpy.random.get_state()
training_size = int(train_precent * len(X))
numpy.random.seed(seed)
shuffle_indices = numpy.random.permutation(numpy.arange(len(X)))
X_train = [X[shuffle_indices[i]] for i in range(training_size)]
Y_train = [Y[shuffle_indices[i]] for i in range(training_size)]
X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))]
Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))]
self.train(X_train, Y_train, Y)
numpy.random.set_state(state)
return self.evaluate(X_test, Y_test)
def read_node_label(filename, skip_head=False):
fin = open(filename, 'r')
X = []
Y = []
while 1:
if skip_head:
fin.readline()
l = fin.readline()
if l == '':
break
vec = l.strip().split(' ')
X.append(vec[0])
Y.append(vec[1:])
fin.close()
return X, Y
GraphEmbedding-master/examples/deepwalk.py 0 → 100644
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# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 701-710.(http://www.perozzi.net/publications/14_kdd_deepwalk.pdf)
"""
import walker
from gensim.models import Word2Vec
import pandas as pd
class DeepWalk:
def __init__(self, graph, walk_length, num_walks, workers=16):
self.graph = graph
self.w2v_model = None
self._embeddings = {}
self.walker = walker.RandomWalker(
graph, p=1, q=1, )
self.sentences = self.walker.simulate_walks(
num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1)
def train(self, embed_size=6, window_size=5, workers=16, iter=4, **kwargs):
kwargs["sentences"] = self.sentences
kwargs["min_count"] = kwargs.get("min_count", 0)
kwargs["size"] = embed_size
kwargs["sg"] = 1 # skip gram
kwargs["hs"] = 1 # deepwalk use Hierarchical Softmax
kwargs["workers"] = workers
kwargs["window"] = window_size
kwargs["iter"] = iter
print("Learning embedding vectors...")
model = Word2Vec(**kwargs)
print("Learning embedding vectors done!")
self.w2v_model = model
return model
def get_embeddings(self,):
if self.w2v_model is None:
print("model not train")
return {}
self._embeddings = {}
for word in self.graph.nodes():
self._embeddings[word] = self.w2v_model.wv[word]
return self._embeddings
GraphEmbedding-master/examples/deepwalk_wiki.py 0 → 100644
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import numpy as np
import time
from sklearn.linear_model import LogisticRegression
import classify
import deepwalk
import matplotlib.pyplot as plt
import networkx as nx
from sklearn.manifold import TSNE
from gensim.models import Word2Vec
from multiprocessing import Pool
from itertools import repeat
def evaluate_embeddings(embeddings):
X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
tr_frac = 0.8
print("Training classifier using {:.2f}% nodes...".format(
tr_frac * 100))
clf = classify.Classifier(embeddings=embeddings, clf=LogisticRegression())
clf.split_train_evaluate(X, Y, tr_frac)
def read(arr):
G = nx.Graph()
for a in arr:
print(a[0])
if not G.has_node(a[0]):
G.add_node(a[0])
if not G.has_node(a[1]):
G.add_node(a[1])
G.add_edge(a[0],a[1],weight=1)
return G
def plot_embeddings(embeddings,):
X, Y = classify.read_node_label('../data/wiki/wiki_labels.txt')
emb_list = []
for k in X:
emb_list.append(embeddings[k])
emb_list = np.array(emb_list)
print(emb_list.shape)
model = TSNE(n_components=2)
node_pos = model.fit_transform(emb_list)
color_idx = {}
for i in range(len(X)):
color_idx.setdefault(Y[i][0], [])
color_idx[Y[i][0]].append(i)
for c, idx in color_idx.items():
plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
plt.legend()
plt.show()
def Affect(z,i):
#for i in z.wv.index2entity:
# key = i + 1
# if key in z.wv.index2entity:
print(i)
print(z.index2entity.index(i))
# print(z.wv.vectors[z.wv.index2entity.index(key)])
x = z.index2entity.index(i)
return z.vectors[x]
def read_all():
data = np.load('graph/test_Bip.npy',allow_pickle=True)
id=0
results = [] #a retirer
print(len(data),len(data[0]),data[0])
for x in data:
w = time.time()
G=read(x)
#print("A",len(G))
model = deepwalk.DeepWalk(G, walk_length=5, num_walks=4, workers=1)
z = model.train(window_size=5, iter=3)
print(len(z.wv.vectors))
result = np.zeros((116835, int(6)))
for i in z.wv.index2entity:
x = z.wv.index2entity.index(i)
result[i] = z.wv.vectors[x]
results.append(result)
name = str('graph/data_train.npy')
name = name[:name.index('.')]
np.save(name+"\\transformed_"+str(id),results)
print(id, "DONE")
id += 1
np.save('graph/test_train.npy', results)
return model
if __name__ == "__main__":
'''G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
model = deepwalk.DeepWalk(G, walk_length=15, num_walks=80, workers=1)
z = model.train(window_size=5, iter=3)
print(len(z.wv.vectors))
result = np.zeros((2405, int(64)))
for i in range(len(z.wv.vectors)):
key = str(i + 1)
if key in z.wv.index2entity:
result[i] = z.wv.vectors[z.wv.index2entity.index(key)]
result = np.asarray(result)
print("result shape",result.shape)'''
model = read_all()
#embeddings = model.get_embeddings()
#print(embeddings)
#evaluate_embeddings(embeddings)
#plot_embeddings(embeddings)
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