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Found 14 papers, 5 papers with code
Is Factuality Decoding a Free Lunch for LLMs? Evaluation on Knowledge Editing Benchmark
The rapid development of large language models (LLMs) enables them to convey factual knowledge in a more human-like fashion.
Graph Descriptive Order Improves Reasoning with Large Language Model
In this work, we reveal the impact of the order of graph description on LLMs' graph reasoning performance, which significantly affects LLMs' reasoning abilities.
LPNL: Scalable Link Prediction with Large Language Models
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.
SLANG: New Concept Comprehension of Large Language Models
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).
Learning node embeddings via summary graphs: a brief theoretical analysis
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.
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Given a multivariate big time series, can we detect anomalies as soon as they occur?
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
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.
Summarizing graphs using configuration model
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
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA
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.
HoloScope: Topology-and-Spike Aware Fraud Detection
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
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
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.
Single-Pass PCA of Large High-Dimensional Data
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning.
Marked Temporal Dynamics Modeling based on Recurrent Neural Network
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.
Predict Anchor Links across Social Networks via an Embedding Approach
Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation.
