no code implementations • 30 Mar 2024 • Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Xueqi Cheng
The rapid development of large language models (LLMs) enables them to convey factual knowledge in a more human-like fashion.
no code implementations • 11 Feb 2024 • Yuyao Ge, Shenghua Liu, Wenjie Feng, Lingrui Mei, Lizhe Chen, Xueqi Cheng
In this work, we reveal the impact of the order of graph description on LLMs' graph reasoning performance, which significantly affects LLMs' reasoning abilities.
no code implementations • 24 Jan 2024 • Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Xueqi Cheng
This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs.
1 code implementation • 23 Jan 2024 • Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Xueqi Cheng
The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs).
no code implementations • 4 Jul 2022 • Houquan Zhou, Shenghua Liu, Danai Koutra, HuaWei Shen, Xueqi Cheng
Recent works try to improve scalability via graph summarization -- i. e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.
no code implementations • 18 Apr 2022 • Quan Ding, Shenghua Liu, Bin Zhou, HuaWei Shen, Xueqi Cheng
Given a multivariate big time series, can we detect anomalies as soon as they occur?
1 code implementation • 3 Dec 2020 • Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, HuaWei Shen, Wenjian Yu, Xueqi Cheng
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
no code implementations • 19 Oct 2020 • Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, HuaWei Shen, Xueqi Cheng
As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.
Social and Information Networks
1 code implementation • 4 Sep 2020 • Xiangyun Ding, Wenjian Yu, Yuyang Xie, Shenghua Liu
The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy.
1 code implementation • 6 May 2017 • Shenghua Liu, Bryan Hooi, Christos Faloutsos
Hence, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users.
Social and Information Networks
1 code implementation • 1 May 2017 • Yongqing Wang, HuaWei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng
However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade— one sharing behavior could be triggered by its non-immediate predecessor in the memory chain.
no code implementations • 25 Apr 2017 • Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, Yaohang Li
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning.
no code implementations • 14 Jan 2017 • Yongqing Wang, Shenghua Liu, Hua-Wei Shen, Xue-Qi Cheng
Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them.
no code implementations • Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) 2016 • Tong Man, Hua-Wei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng
Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation.