Few-Shot Stance Detection via Target-Aware Prompt Distillation

Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. In this paper, we propose label-consistency based graph neural network(LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs. Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.We further show the superiority of LC-GNN in sparse scenarios with only a handful of labeled nodes.

Coupled Graph Neural Networks for Predicting the Popularity of Online Content

Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation. Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction. These methods ignore the interaction between early adopters and potential adopters or the interactions among potential adopters over social networks. Consequently, they fail to capture the cascading effect triggered by early adopters in social networks, and thus have limited predictive power. In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks among users for popularity prediction. We propose a novel method, namely Coupled-GNNs, which use two coupled graph neural networks to capture the cascading effect in information diffusion. One graph neural network models the interpersonal influence, gated by the adoption state of users. The other graph neural network models the adoption state of users via interpersonal influence from their neighbors. Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks. Experiments conducted on both synthetic data and real-world Sina Weibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.

A Graph Auto-Encoder for Attributed Network Embedding

Attributed network embedding aims to learn low-dimensional node representations from both network structure and node attributes. Existing methods can be categorized into two groups: (1) the first group learns two separated node representations from network structure and node attribute respectively and concatenating them together; (2) the other group obtains node representations by translating node attributes into network structure or vice versa. However, both groups have their drawbacks. The first group neglects the correlation between these two types of information, while the second group assumes strong dependence between network structure and node attributes. In this paper, we address attributed network embedding from a novel perspective, i.e., learning representation of a target node via modeling its attributed local subgraph. To achieve this goal, we propose a novel graph auto-encoder framework, namely GraphAE. For a target node, GraphAE first aggregates the attribute information from its attributed local subgrah, obtaining its low-dimensional representation. Next, GraphAE diffuses its representation to nodes in its local subgraph to reconstruct their attribute information. Our proposed perspective transfroms the problem of learning node representations into the problem of modeling the context information manifested in both network structure and node attributes, thus having high capacity to learn good node representations for attributed network. Extensive experimental results on real-world datasets demonstrate that the proposed framework outperforms the state-of-the-art network approaches at the tasks of link prediction and node classification.