User Profile Preserving Social Network Embedding
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks. |
| Researcher Affiliation | Collaboration | a Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Australia b Data61, CSIRO, Australia c Dept. of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, USA d School of Computer Science, Fudan University, China |
| Pseudocode | Yes | Algorithm 1 The UPP-SNE Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the methodology described. |
| Open Datasets | Yes | We perform experiments on four real-world social networks listed as follows: Google+1 is an ego-network of a Google+ user and nodes in this network represent the user s friends. There are 1206 nodes and 66918 links in this network. We use people s gender as class label. Each node is described by a 940-dimensional bag-of-words vector constructed from treestructured user profiles [Leskovec and Mcauley, 2012]. Ego-Facebook1 is an ego-network of a Facebook user. This network consists of 755 nodes and 60050 links. People s education type is used as class label. Each node is described by a 477-dimensional vector [Leskovec and Mcauley, 2012]. Hamilton and Rochester2 are two of the collection of 100 US university Facebook networks [Traud et al., 2012]. The two networks contain 2118 nodes, 87486 edges, and 4145 nodes, 145305 edges respectively. Each node s profile is described by 7 user attributes: student/faculty status flag, gender, major, second major/minor, dorm/house, year, and high school. We select student/faculty status flag as class label and construct a 144-dimensional, and a 235-dimensional binary feature vector for Hamilton and Rochester, respectively. |
| Dataset Splits | Yes | To make fair comparisons, we vary the training ratio from 1% to 10% by an increment of 1%. For each training ratio, we randomly split the training set and test set for 10 times and report the averaged accuracy. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For UPP-SNE, we set the number of random walks per node γ as 40, the walk length l as 40, the window size t as 10, and the number of iterations as 40. All baselines use default parameters as reported. The dimension of learned node representations m is set as 128 for all the algorithms. |