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This is a sample implementation of "TIMERS: Error-Bounded SVD Restart on Dynamic Networks"(AAAI 2018).

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network-embedding network-representation-learning dynamic-network svd

timers's Introduction

TIMERS

This is a sample implementation of "TIMERS: Error-Bounded SVD Restart on Dynamic Networks"(AAAI 2018).

Requirements

MATLAB (MATLAB 2017a works fine for me)

Usage

Example Usage
See TIMERS_Sample.m for a sample run on the syntactic network
Functions
TIMERS_Sample.m: a sample run on the syntactic networks, see annotations for detail
RefineBound.m: calculate the lower bound of the objective function, our main results (see Eq. (13) in our paper)
Random_Com.m: generate a random graph with communities forming (see RANDOM-Com Dataset in our paper)
Obj.m: input a similarity matrix S and two embeddings U,V, return || S - U * V ||_F^2, using a trick to reduce memory cost
Obj_SimChange.m: returns the new objective function when only S changes

Cite

If you find this code useful, please cite our paper:

@inproceedings{zhang2018timers,
  title={TIMERS: Error-Bounded SVD Restart on Dynamic Networks},
  author={Zhang, Ziwei and Cui, Peng and Pei, Jian and Wang, Xiao and Zhu, Wenwu},
  booktitle={Proceedings of the 32nd AAAI Conference on Artificial Intelligence},
  year={2018},
  organization={AAAI}
}

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