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Dec 21, 2018
Dec 21, 2018

HHNE

Source code for AAAI-2019 paper "Hyperbolic Heterogeneous Information Network Embedding"

The HHNE code is built upon Mikolov et al.'s word2vec.c from https://code.google.com/archive/p/word2vec/

Compile

  1. cmd "make" in folder HHNE/code/
  2. run "hhne" in folder HHNE/code/

Usage

./hhne [options]

The Options follow Mikolov et al.'s word2vec Options for training:

-train <file>
	Use text data from <file> to train the model
-output <file>
	Use <file> to save the resulting word vectors / word clusters
-size <int>
	Set size of word vectors; default is 100
-window <int>
	Set max skip length between words; default is 5
-sample <float>
	Set threshold for occurrence of words. Those that appear with higher frequency in the training data
	will be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)
-negative <int>
	Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)
-threads <int>
	Use <int> threads (default 12)
-iter <int>
	Run more training iterations (default 5)
-min-count <int>
	This will discard words that appear less than <int> times; default is 5
-alpha <float>
	Set the starting learning rate; default is 0.025 for skip-gram
-debug <int>
	Set the debug mode (default = 2 = more info during training)
-save-vocab <file>
	The vocabulary will be saved to <file>
-read-vocab <file>
	The vocabulary will be read from <file>, not constructed from the training data

Examples:

./hhne -train random_walks.txt -output hhne.embeddings -size 2 -window 5 -negative 10 -threads 32 -iter 5 -alpha 0.025

Input:

The walks generated by meta-path guided random walks, each of which consists of different types of nodes.

Output:

The file for each node in text format (e.g., hhne.embeddings.txt)

HHNE bibtex information

@inproceedings{HHNE:AAAI19,
 title={Hyperbolic Heterogeneous Information Network Embedding},
 author = {Wang, Xiao and Zhang, Yiding and Shi, Chuan},
 booktitle = {AAAI},
 year = {2019},
} 

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Source code for AAAI 2019 paper "Hyperbolic Heterogeneous Information Network Embedding"

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