This code is adapted from DeepCas as described in the paper:
Prediction of Information Cascades via Content and Structure Proximity Preserved Graph Level Embedding
Xiaodong Feng, Qihang Zhao, Zhen Liu.
Under the second-round review of Information Sciences.
#### Options
You can check out the other options available to use with *DeepCas* using:<br/>
```{r, engine='bash', count_lines}
python gen_walks/gen_walks.py --help
th main/run.lua --help
global_graph.txt lists each node's neighbors in the global network:
node_id \t\t (null|neighbor_id:weight \t neighbor_id:weight...)
"\t" means tab, and "null" is used when a node has no neighbors.
cascade_(train|val|test).txt list cascades, one cascade per line:
cascade_id \t starter_id... \t constant_field \t num_nodes \t source:target:weight... \t label...
"starter_id" are nodes who start the cascade, "num_nodes" counts the number of nodes in the cascade.
cascade.txt:cascade_train.txt+cascade_val.txt+cascade_test.txt
Since we can predict cascade growth at different timepoints, there could be multiple labels.
Tensorflow 1.11.1
To run DeepCas tensorflow version on a test data set, execute the following command:
cd DeepCas
python gen_walks/gen_walks.py --dataset test-net
python gen_con.py
python gen_str.py
cd tensorflow
python preprocess.py
python run.py
If you find DeepCon+Str useful for your research, please consider citing the following paper: Xiaodong Feng, Qihang Zhao, Zhen Liu. Prediction of Information Cascades via Content and Structure Proximity Preserved Graph Level Embedding, Under the second-round review of Information Sciences.
Please send any questions you might have about the code and/or the algorithm to fengxd1988@hotmail.com.