HID: Hierarchical Multiscale Representation Learning for Information Diffusion
Authors: Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets showcase the superiority of our method. |
| Researcher Affiliation | Academia | Department of Computer Science, Rutgers University, New Brunswick, NJ 08901 {honglu.zhou, shuyuan.xu, zuohui.fu}@rutgers.edu, gdm@demelo.org, yongfeng.zhang@rutgers.edu, mk1353@cs.rutgers.edu |
| Pseudocode | Yes | Algorithm 1 HID(s, p, d, Dtrain, F) and Algorithm 2 UPSCALING(s, p, Dtrain) |
| Open Source Code | Yes | 1https://github.com/hongluzhou/HID |
| Open Datasets | Yes | Memetracker [Leskovec et al., 2009]., Twitter [Yang and Leskovec, 2011]., Digg [Hogg and Lerman, 2012]. |
| Dataset Splits | Yes | For each dataset, the set of diffusion paths is randomly split into two parts: 80% for training and validation (Dtrain), and the remainder for testing (Dtest). The hyper-parameters are chosen based on validation performance. |
| Hardware Specification | Yes | All models run on a single machine with 256 GB memory, 48 CPU cores at 2.30GHz, and an NVIDIA Quadro K6000 graphics card. |
| Software Dependencies | No | The paper mentions the models used (CDK, CSDK, Forest, HARP, Walklets) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For CDK, the maximum training epoch was 8,000 and per epoch the number of samples was 5,000. The initial learning rate was 0.01 with a decay of 1 10 6. CSDK shared the same parameters, except 10,000 for the number of samples per epoch and 1 10 12 for decay. For Forest, HARP, and Walklets, we used the parameters suggested by the authors. Forest used a maximum training epoch of 24. The user representation dimensionality was 64. HID requires two hyper-parameters, s and p. For results in Table 2, different values of s in {1, 2, 3} and different values of p in {1.2, 1.5, 2, 3, 4} were tried with a grid search using the validation data before choosing the best-performing settings |