Two papers accepted in IEEE ICDM 2021

1
Title: Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks
Author: Yoonsuk Kang, Woncheol Lee, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim
Abstract
In this paper, we propose a framework for embedding-based community detection on signed networks. It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm (e.g., k-means) on vectors, thereby detecting a community structure in the network. When performing the embedding process, our framework learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network, our framework learns not only the edges in balanced real-triangles but those in balanced virtual-triangles that are produced by our generator. Finally, our framework employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that our framework consistently and significantly outperforms the state-of-the-art community detection methods in all datasets.
2
Title: MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems
Author: Yunyong Ko, Jae-Seo Yu, Hong-Kyun Bae, Yongjun Park, Dongwon Lee, and Sang-Wook Kim
Abstract
In recent years, quantization methods have successfully accelerated the training of large deep neural network (DNN) models by reducing the level of precision in computing operations (e.g., forward/backward passes) without sacrificing its accuracy. In this work, therefore, we attempt to apply such a quantization idea to the popular Matrix factorization (MF) methods to deal with the growing scale of models and datasets in recommender systems. However, to our dismay, we observe that the state-of-the-art quantization methods are not effective in the training of MF models, unlike their successes in the training of DNN models. To this phenomenon, we posit that two distinctive features in training MF models could explain the difference: (i) the training of MF models is much more memory-intensive than that of DNN models, and (ii) the quantization errors across users and items in recommendation are not uniform. From these observations, we develop a quantization framework for MF models, named MASCOT, employing novel strategies (i.e., m-quantization and g-switching) to successfully address the aforementioned limitations of quantization in the training of MF models. The comprehensive evaluation using four real-world datasets demonstrates that MASCOT improves the training performance of MF models by about 45%, compared to the training without quantization, while maintaining low model errors, and the strategies and implementation optimizations of MASCOT are quite effective in the training of MF models. For the detailed information about MASCOT, we release the code of MASCOT and the datasets at: https://github.com/Yujaeseo/lCDM-2021_MASCOT.