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1. Graph Convolutional Networks for Student Answers Assessment

   Ait Khayi, Nisrine; Rus, Vasile (October 2020, 23rd International Conference on Text, Speech and Dialogue)

   Graph Convolutional Networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use Graph Convolutional Networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of Graph Convolutional Networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%. 

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2. Learning Signed Network Embedding via Graph Attention

   https://doi.org/10.1609/aaai.v34i04.5911  

   Li, Yu; Tian, Yuan; Zhang, Jiawei; Chang, Yi (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets. 

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3. Graph Few-shot Learning with Task-specific Structures

   Wang, Song; Chen, Chen; Li, Jundong (January 2022, Advances in neural information processing systems)

   Graph few-shot learning is of great importance among various graph learning tasks. Under the few-shot scenario, models are often required to conduct classification given limited labeled samples. Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) and perform classification across a series of meta-tasks. Nevertheless, these methods generally rely on the original graph (i.e., the graph that the meta-task is sampled from) to learn node representations. Consequently, the learned representations for the same nodes are identical in all meta-tasks. Since the class sets are different across meta-tasks, node representations should be task-specific to promote classification performance. Therefore, to adaptively learn node representations across meta-tasks, we propose a novel framework that learns a task-specific structure for each meta-task. To handle the variety of nodes across meta-tasks, we extract relevant nodes and learn task-specific structures based on node influence and mutual information. In this way, we can learn node representations with the task-specific structure tailored for each meta-task. We further conduct extensive experiments on five node classification datasets under both single- and multiple-graph settings to validate the superiority of our framework over the state-of-the-art baselines. 

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4. Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups

   https://doi.org/10.1109/ICDMW60847.2023.00050  

   Stephens, Anna; Santos, Francisco; Tan, Pang-Ning; Esfahanian, Abdol-Hossein (December 2023, IEEE International Conference on Data Mining Workshops)

   Jihe Wang, Yi He (Ed.)

   Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks. Cross-network node classification extends GNN techniques to account for domain drift, allowing for node classification on an unlabeled target network. In this paper we present OTGCN, a powerful, novel approach to cross-network node classification. This approach leans on concepts from graph convolutional networks to harness insights from graph data structures while simultaneously applying strategies rooted in optimal transport to correct for the domain drift that can occur between samples from different data collection sites. This blended approach provides a practical solution for scenarios with many distinct forms of data collected across different locations and equipment. We demonstrate the effectiveness of this approach at classifying Autism Spectrum Disorder subjects using a blend of imaging and non-imaging data. 

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5. ORIGIN: Non-Rigid Network Alignment

   https://doi.org/10.1109/BigData47090.2019.9005663  

   Zhang, Si; Tong, Hanghang; Xu, Jiejun; Hu, Yifan; Maciejewski, Ross (December 2019, BigData)

   Network alignment is a fundamental task in many high-impact applications. Most of the existing approaches either explicitly or implicitly consider the alignment matrix as a linear transformation to map one network to another, and might overlook the complicated alignment relationship across networks. On the other hand, node representation learning based alignment methods are hampered by the incomparability among the node representations of different networks. In this paper, we propose a unified semi-supervised deep model (ORIGIN) that simultaneously finds the non-rigid network alignment and learns node representations in multiple networks in a mutually beneficial way. The key idea is to learn node representations by the effective graph convolutional networks, which subsequently enable us to formulate network alignment as a point set alignment problem. The proposed method offers two distinctive advantages. First (node representations), unlike the existing graph convolutional networks that aggregate the node information within a single network, we can effectively aggregate the auxiliary information from multiple sources, achieving far-reaching node representations. Second (network alignment), guided by the highquality node representations, our proposed non-rigid point set alignment approach overcomes the bottleneck of the linear transformation assumption. We conduct extensive experiments that demonstrate the proposed non-rigid alignment method is (1) effective, outperforming both the state-of-the-art linear transformation-based methods and node representation based methods, and (2) efficient, with a comparable computational time between the proposed multi-network representation learning component and its single-network counterpart. 

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