TransConv: Relationship Embedding in Social Networks
Authors: Yi-Yu Lai, Jennifer Neville, Dan Goldwasser4130-4138
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real social network data show Trans Conv learns better user and relationship embeddings compared to other state-of-the-art knowledge graph embedding models. Moreover, the results illustrate that our model is more robust for sparse relationships where there are fewer examples. |
| Researcher Affiliation | Academia | Yi-Yu Lai, Jennifer Neville, Dan Goldwasser Department of Computer Science Purdue University, West Lafayette, IN 47907, USA {lai49, neville, dgoldwas}@purdue.edu |
| Pseudocode | No | The paper describes the model and its optimization mathematically and verbally, but it does not include any clearly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code or a link to a repository for the methodology described. |
| Open Datasets | Yes | We analyze two social network datasets in our experiments: The public Purdue Facebook network data from March 2007 to March 2008... Our Twitter dataset is sampled from the dataset collected by (Kwak et al. 2010). It contains 20 million post activities from June to July 2009. There are 300,985 triplets with 22,729 users. We use the posts with user mentions (e.g., @david happy birthday! ) as textual interactions. The 42 relationships types are constructed from user profiles and follower/following information. ... Dataset #User #Rel #Train #Valid #Test Facebook 19,409 41 126,963 42,101 42,102 Twitter 22,729 42 180,606 60,189 60,190 |
| Dataset Splits | Yes | We analyze two social network datasets in our experiments: The public Purdue Facebook network data from March 2007 to March 2008... Our Twitter dataset is sampled from the dataset collected by (Kwak et al. 2010). ... Dataset #User #Rel #Train #Valid #Test Facebook 19,409 41 126,963 42,101 42,102 Twitter 22,729 42 180,606 60,189 60,190 ... we perform stratified sampling to split the dataset and use training set and validation set to select the best configurations. |
| Hardware Specification | No | The paper discusses the experimental process and settings, but it does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Google’s pre-trained Skip-Gram model (Mikolov et al. 2013)" but does not provide specific version numbers for any software, libraries, or dependencies used in their implementation. |
| Experiment Setup | Yes | In training Trans Conv, we perform grid search over learning rate R for SGD among {0.001, 0.005, 0.01}, the batch size B among {100, 500}, the number of training epochs T among {200, 500}, the margin γ among {0.5, 1.0, 1.5}, the embedding dimension k among {100, 200, 300}, the norm used in score function among {L1-norm, L2-norm}, the top K TF-IDF among {100, 500, 1000, 1500, 2000, 2500} and the learning weight α for conversation factors between 0 and 1. For the Facebook dataset, the optimal configurations of Trans Conv are: R = 0.001, B = 100, T = 500, γ = 1.0, k = 300, norm = L1-norm, K = 2000 and α = 0.5. The same configurations are applied to the Twitter dataset. |