Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
User Profile Preserving Social Network Embedding
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
IJCAI 2017 | Venue PDF | 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. |