Graph Few-Shot Learning via Knowledge Transfer
Authors: Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, Zhenhui Li6656-6663
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.In this section, we conduct extensive experiments to demonstrate the benefits of GFL, with the goal of answering the following questions: (1) Can GFL outperform baseline meth- Table 1: Data Statistics. |
| Researcher Affiliation | Collaboration | 1Pennsylvania State University, 2University of Notre Dame, 3Tencent AI Lab |
| Pseudocode | Yes | Algorithm 1 Training Process of GFL |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use four datasets of different kinds of graphs: Collaboration, Reddit, Citation and Pubmed. (1): Collaboration data: Our first task is to predict research domains of different academic authors. We use the collaboration graphs extracted from the AMiner data (AMi 2019). (2): Reddit data: In the second task, we predict communities of different Reddit posts. We construct post-to-post graphs from Reddit community data (Hamilton, Ying, and Leskovec 2017). (4): Pubmed data: Similar to the third task, the last task is to predict paper class labels. The difference is that the citation graphs are extracted from the Pub Med database (Veliˇckovi c et al. 2018) |
| Dataset Splits | Yes | For each graph, N labeled nodes for each class are provided as support set. The rest nodes are used as query set for evaluating the performance. Table 1: Data Statistics... # Graphs (Meta-train), # Graphs (Meta-val.), # Graphs (Meta-test) |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like GCN but does not provide specific version numbers for any libraries, frameworks, or programming languages used (e.g., 'two-layer graph convolutional structure (GCN)'). |
| Experiment Setup | Yes | Like (Kipf and Welling 2017), the embedding structure (i.e., θ in Eqn. (3)) is a two-layer graph convolutional structure (GCN) with 32 neurons in each layer. For PGNN in Eqn. (3), each AGNN in Eqn. (4) and each FGNN in Eqn. (5), we use one-layer GCN as the proxy of GNN. The distance metric d is defined as the inner product distance. For our proposed GFL, we use GFL-mean and GFL-att to represent the type of hierarchical representation aggregator (i.e., GFL-mean represents mean pooling aggregator in Eqn. (6) and GFL-att represents attention aggregator in Eqn. (7)). The threshold μ0 in Section 4.1 for constructing relation structure of support set is set as 0.5. Algorithm 1 Training Process of GFL ... α: stepsize; γ: balancing parameter for loss |