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Word Embeddings
The dataset is a 11463 × 5812 matrix of word counts, where the rows represent
words, and columns represent conference papers. We will use this to construct a
graph of papers, where an edge between two papers represents a word that occurs in
both of them. Both node2vec and DeepWalk assume that the graph is undirected and
unweighted. Our graph is undirected, since a relationship between a pair of papers is
bidirectional. However, our edges could have weights based on the number of word
co-occurrences between the two documents. For our example, we will consider any
number of co-occurrences above 0 to be a valid unweighted edge.
As usual, we will start by declaring our imports:
import gensim
import logging
import numpy as np
import os
import shutil
import tensorflow as tf
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import cosine_similarity
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)
s', level=logging.INFO)
The next step is to download the data from the UCI repository and convert it to
a sparse term document matrix, TD, then construct a document-document matrix
E by multiplying the transpose of the term-document matrix with itself. Our graph
is represented as an adjacency or edge matrix by the document-document matrix.
Since each element represents a similarity between two documents, we will binarize
the matrix E by setting any non-zero elements to 1:
DATA_DIR = "./data"
UCI_DATA_URL = "https://archive.ics.uci.edu/ml/machine-learningdatabases/00371/NIPS_1987-2015.csv"
def download_and_read(url):
local_file = url.split('/')[-1]
p = tf.keras.utils.get_file(local_file, url, cache_dir=".")
row_ids, col_ids, data = [], [], []
rid = 0
f = open(p, "r")
for line in f:
line = line.strip()
if line.startswith("\"\","):
# header
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