discovery of influencers by using association rule techniques to extract the hidden relationships between users. (评估方法)To evaluate thefeasibilityand effectiveness of our approach, we propose a new centrality measure called the completeness centrality, and perform an evaluation based on a case study ...
【98论文泛读】GRAPH-BASED KNOWLEDGE TRACING: MODELING STUDENT PROFICIENCY USING GRAPH NEURAL NETWORK 小z呀 凭君莫话封侯事, 一将功成万骨枯。 问题: 以前的深度学习知识追踪方法DKT和DKVMN没有考虑知识之间的图结构关系,导致模型表达能力较弱。 这篇论文提出了一种基于图的知识追踪方法GKT,主要内容包括: 将...
论文阅读Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network,程序员大本营,技术文章内容聚合第一站。
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macro...
To show you the true power of graph data modeling, we’re going to look at how we model a domain using both relational- and graph-based techniques. You’re probably already familiar with RDBMS data modeling techniques, so this comparison will highlight a few similarities – and many differen...
good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine text-based approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models ...
[论文解读]Graph-Based Parallel Large Scale Structure from Motion,程序员大本营,技术文章内容聚合第一站。
Nakagawa, H., Iwasawa, Y., Matsuo, Y.: Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 156–163. ACM (2019) Google Scholar Pardos, Z.A., Heffernan, N.T.: Modeling individualization in...
Charge transport in molecular solids, such as semiconducting polymers, is strongly affected by packing and structural order over several length scales. Conventional approaches to modeling these phenomena range from analytical models to numerical models u
20 proposed a VAE-based iterative generative model for small graphs. They restrict themselves to modeling only the graph structure, not a full graph including node and edge features for molecule generation. Liu et al.21 proposed a graph neural network model based on normalizing flows for memory-...