We propose a graph-based representation learning framework for video
summarization. First, we convert an input video to a graph where nodes
correspond to each of the video frames. Then, we impose sparsity on the graph
by connecting only those pairs of nodes that are within a specified temporal
distance. We then formulate the video summarization task as a binary node
classification problem, precisely classifying video frames whether they should
belong to the output summary video. A graph constructed this way aims to
capture long-range interactions among video frames, and the sparsity ensures
the model trains without hitting the memory and compute bottleneck. Experiments
on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed
nimble model compared to existing state-of-the-art summarization approaches
while being one order of magnitude more efficient in compute time and memory

提出了一种基于图的表示学习框架用于视频摘要，通过将输入视频转换为图，从而在图上建立稀疏连接，将视频摘要任务转化为二进制节点分类问题，以捕捉视频帧之间的长程相互作用，同时具有更高的计算效率和内存利用率。

VideoSAGE: 利用图表示学习进行视频摘要

VideoSAGE: Video Summarization with Graph Representation Learning

Stock trend classification remains a fundamental yet challenging task, owing
to the intricate time-evolving dynamics between and within stocks. To tackle
these two challenges, we propose a graph-based representation learning approach
aimed at predicting the future movements of multiple stocks. Initially, we
model the complex time-varying relationships between stocks by generating
dynamic multi-relational stock graphs. This is achieved through a novel edge
generation algorithm that leverages information entropy and signal energy to
quantify the intensity and directionality of inter-stock relations on each
trading day. Then, we further refine these initial graphs through a stochastic
multi-relational diffusion process, adaptively learning task-optimal edges.
Subsequently, we implement a decoupled representation learning scheme with
parallel retention to obtain the final graph representation. This strategy
better captures the unique temporal features within individual stocks while
also capturing the overall structure of the stock graph. Comprehensive
experiments conducted on real-world datasets from two US markets (NASDAQ and
NYSE) and one Chinese market (Shanghai Stock Exchange: SSE) validate the
effectiveness of our method. Our approach consistently outperforms
state-of-the-art baselines in forecasting next trading day stock trends across
three test periods spanning seven years. Datasets and code have been released
(this https URL).

股票趋势分类是一项基本但具有挑战性的任务，本文提出了一种基于图表示学习的方法来预测多个股票未来的走势，通过生成动态的多关系股票图来建模股票之间复杂的时变关系，然后通过随机多关系扩散过程进一步改进这些初始图，并实现并行保留的解耦表示学习方案来获取最终的图表示。在美国（纳斯达克和纽约证券交易所）和中国（上海证券交易所）的真实数据集上进行的综合实验验证了我们方法的有效性，这一方法在涵盖七年的三个测试期间的预测下一个交易日股票趋势方面一直优于现有技术基线模型。

多关系图扩散神经网络与并行保留用于股票趋势分类

Multi-relational Graph Diffusion Neural Network with Parallel Retention  for Stock Trends Classification

Message-passing neural networks (MPNNs) have been successfully applied to
representation learning on graphs in a variety of real-world applications.
However, two fundamental weaknesses of MPNNs' aggregators limit their ability
to represent graph-structured data: losing the structural information of nodes
in neighborhoods and lacking the ability to capture long-range dependencies in
disassortative graphs. Few studies have noticed the weaknesses from different
perspectives. From the observations on classical neural network and network
geometry, we propose a novel geometric aggregation scheme for graph neural
networks to overcome the two weaknesses. The behind basic idea is the
aggregation on a graph can benefit from a continuous space underlying the
graph. The proposed aggregation scheme is permutation-invariant and consists of
three modules, node embedding, structural neighborhood, and bi-level
aggregation. We also present an implementation of the scheme in graph
convolutional networks, termed Geom-GCN (Geometric Graph Convolutional
Networks), to perform transductive learning on graphs. Experimental results
show the proposed Geom-GCN achieved state-of-the-art performance on a wide
range of open datasets of graphs. Code is available at
this https URL

本文提出了一种新的基于几何学聚合的 aggregation 方案，包含三个模块：节点嵌入，结构化邻域和双层聚合，并将其实现到图卷积网络中，命名为 Geom-GCN。在多个开放数据集上的实验结果表明，Proposed Geom-GCN 已经达到了当前状态的最佳性能。