In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.

As multimodal learning finds applications in a wide variety of high-stakes societal tasks, investigating their robustness becomes important. Existing work has focused on understanding the robustness of vision-and-language models to imperceptible variations on benchmark tasks. In this work, we investigate the robustness of multimodal classifiers to cross-modal dilutions - a plausible variation. We develop a model that, given a multimodal (image + text) input, generates additional dilution text that (a) maintains relevance and topical coherence with the image and existing text, and (b) when added to the original text, leads to misclassification of the multimodal input. Via experiments on Crisis Humanitarianism and Sentiment Detection tasks, we find that the performance of task-specific fusion-based multimodal classifiers drops by 23.3% and 22.5%, respectively, in the presence of dilutions generated by our model. Metric-based comparisons with several baselines and human evaluations indicate that our dilutions show higher relevance and topical coherence, while simultaneously being more effective at demonstrating the brittleness of the multimodal classifiers. Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations, especially in human-facing societal applications. The code and other resources are available at https://claws-lab.github.io/multimodal-robustness/.

基于会话的建议系统在会话中捕获用户的短期兴趣。会话上下文（即，会话中用户在会话中的高级兴趣或意图）在大多数数据集中都没有明确给出，并且隐式推断会话上下文作为项目级属性的汇总是粗略的。在本文中，我们提出了ISCON，该ISCON隐含地将会议上下文化。ISCON首先通过创建会话信息图，学习图嵌入和聚类来为会话生成隐式上下文，以将会话分配给上下文。然后，ISCON训练会话上下文预测器，并使用预测上下文的嵌入来增强下一项目的预测准确性。四个数据集的实验表明，ISCON比最新模型具有优越的下一项目预测准确性。REDDIT数据集中的ISCON的案例研究证实，分配的会话上下文是独特而有意义的。

网络科学和技术的快速发展取决于可共享的数据集。当前，没有用于报告和共享网络数据集的标准实践。一些网络数据集提供商仅共享链接，而另一些网络数据集提供商提供了一些上下文或基本统计信息。结果，关键信息可能无意间删除，网络数据集消费者可能会误解或忽略关键方面。使用网络数据集不适当地导致严重的后果（例如，歧视），尤其是当将网络上的机器学习模型部署在高维护域中时。挑战出现，因为网络通常在不同的领域（例如网络科学，物理等）上使用并具有复杂的结构。为了促进网络数据集提供商和消费者之间的通信，我们提出了网络报告。网络报告是一个结构化的描述，总结和上下文化网络数据集。网络报告从先前的工作中扩展了数据集报告（例如，数据集的数据表）的想法，其中包含非i.i.d的网络特定说明。自然，人口统计信息，网络特征等。我们希望网络报告鼓励不同领域的网络研发透明度和问责制。

在本文中，我们介绍了对非对称确定点处理（NDPP）的在线和流媒体地图推断和学习问题，其中数据点以任意顺序到达，并且算法被约束以使用单次通过数据以及子线性存储器。在线设置有额外要求在任何时间点维护有效的解决方案。为了解决这些新问题，我们提出了具有理论担保的算法，在几个真实的数据集中评估它们，并显示它们对最先进的离线算法提供了可比的性能，该算法将整个数据存储在内存中并采取多次传递超过它。

Graphs的许多模型属于边缘无关的点产品型号的框架。这些模型输出所有节点之间存在的边缘的概率，并且两个节点之间的链路的概率随与节点相关联的矢量的点乘积而增加。最近的工作表明，这些模型无法捕获实际图中的关键结构，特别是异种结构，其中在不同节点之间发生链接。我们提出了一种独立的图形生成模型，它足以捕捉到异源性，B）产生非负嵌入物，这允许在社区方面解释的链接预测，C）有效地在具有梯度的真实图中优化跨熵损失下降。我们的理论结果展示了我们模型的表现力，其能够使用最大程度的线性的多个簇进行准确地重建图表，以及其在数据中捕获异常和精梳性的能力。此外，我们的实验展示了我们模型对多种重要应用任务等多个重要应用程序任务的有效性，例如多标签聚类和链路预测。

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings. In most cases, the estimated distribution sums to 1 over all finite strings. However, in some pathological cases, probability mass can ``leak'' onto the set of infinite sequences. In order to characterize the notion of leakage more precisely, this paper offers a measure-theoretic treatment of language modeling. We prove that many popular language model families are in fact tight, meaning that they will not leak in this sense. We also generalize characterizations of tightness proposed in previous works.