Yoon-Sik Cho

Seyun Bae, Hoyoon Byun, Changdae Oh et al.

Multimodal machine learning has been widely studied for the development of general intelligence. Recently, the Perceiver and Perceiver IO, show competitive results for diverse dataset domains and tasks. However, recent works, Perceiver and Perceiver IO, have focused on heterogeneous modalities, including image, text, and there are few research works for graph structured datasets. A graph has an adjacency matrix different from other datasets such as text and image, and it is not trivial to handle the topological information. In this study, we provide a Graph Perceiver IO (GPIO), the Perceiver IO for the graph structured dataset. We keep the main structure of the GPIO as the Perceiver IO because the Perceiver IO already handles the diverse dataset well, except for the graph structured dataset. The GPIO is a general method that handles diverse datasets, such as graph-structured data, text, and images, by leveraging positional encoding and output query smoothing. Compared to graph neural networks (GNNs), GPIO requires lower complexity and can efficiently incorporate global and local information, which is also empirically validated through experiments. Furthermore, we propose GPIO+ for the multimodal few-shot classification that incorporates both images and graphs simultaneously. GPIO achieves higher benchmark accuracy than GNNs across multiple tasks, including graph classification, node classification, and multimodal text classification, while also attaining superior AP and AUC in link prediction. Additionally, GPIO+ outperforms GNNs in multimodal few-shot classification. Our GPIO(+) can serve as a general architecture for handling various modalities and tasks.

Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara et al.

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.

Yoon-Sik Cho, Aram Galstyan, P. Jeffrey Brantingham et al.

We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We develop an efficient approximate algorithm based on variational expectation-maximization to infer unknown participants in an event given the location and the time of the event. We validate the model on synthetic as well as real-world data, and obtain very promising results on the identity-inference task. We also use our model to predict the timing and participants of future events, and demonstrate that it compares favorably with baseline approaches.