An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space

Social events reflect changes in communities, such as natural disasters and emergencies. Detection of these situations can help residents and organizations in the community avoid danger and reduce losses. The complex nature of social messages makes social event detection on social media challenging. The challenges that have a greater impact on social media detection models are as follows: (1) the amount of social media data is huge but its availability is small; (2) social media data is a tree structure and traditional Euclidean space embedding will distort embedded features; and (3) the heterogeneity of social media networks makes existing models unable to capture rich information well. To solve the above challenges, we propose a Heterogeneous Information Graph representation via Hyperbolic space combined with an Automatic Meta-path selection (GraphHAM) model, an efficient framework that automatically selects the meta-path's weight and combines hyperbolic space to learn information on social media. In particular, we apply an efficient automatic meta-path selection technique and convert the selected meta-path into a vector, thereby reducing the requisite amount of labeled data for the model. We also design a novel Hyperbolic Multi-Layer Perceptron (HMLP) to further learn the semantic and structural information of social information. Extensive experiments show that GraphHAM can achieve outstanding performance on real-world data using only 20% of the whole dataset as the training set. Our code can be found on GitHub https://github.com/ZITAIQIU/GraphHAM.