It is generally accepted that collaborative information is important for the performance of recommender systems. It is also generally accepted that if this information is sparser, it impacts recommendation systems negatively. Various approaches have tried to lift this problem by employing side information. However, global patterns that can be provided by clusters of similar items and users or even additional information such as text are often not used together with collaborative information. We study the impact of integrating clustering embeddings, review embeddings, and their combinations with embeddings obtained by a recommender system. We study the performance of this approach across various state-of-the-art recommender system algorithms including graph-based methods. We highlight that graph structures are important with sparser datasets and both, in knowledge graphs with side information as well as in collaborative bipartite graphs. In less sparse datasets, a collaborative bipartite graph is usually sufficient. We also highlight that the improvement of recommendation performance through clustering, particularly evident when combined with review embeddings is most visible on sparser data, while on less sparse data incorporating review embeddings may be sufficient when combined with one of the graph-based methods, or otherwise when combined with clustering in other methods.

Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial to know the distinct type of defect, such as a bent, cut, or scratch. The ability to recognize the "exact" defect type enables automated treatments of the anomalies in modern production lines. Current methods are limited to solely detecting whether a product is defective or not without providing any insights on the defect type, nevertheless detecting and identifying multiple defects. We propose MultiADS, a zero-shot learning approach, able to perform Multi-type Anomaly Detection and Segmentation. The architecture of MultiADS comprises CLIP and extra linear layers to align the visual- and textual representation in a joint feature space. To the best of our knowledge, our proposal, is the first approach to perform a multi-type anomaly segmentation task in zero-shot learning. Contrary to the other baselines, our approach i) generates specific anomaly masks for each distinct defect type, ii) learns to distinguish defect types, and iii) simultaneously identifies multiple defect types present in an anomalous product. Additionally, our approach outperforms zero/few-shot learning SoTA methods on image-level and pixel-level anomaly detection and segmentation tasks on five commonly used datasets: MVTec-AD, Visa, MPDD, MAD and Real-IAD.

Attributed multiplex networks are powerful representations of complex systems where nodes represent entities, their attributes represent the properties, and each type of interaction is modeled as a relationship (layer) in a network. To analyze these networks, it is crucial to find a meaningful representation of nodes, node attributes, and class labels into a joint low-dimensional space. To this end, we propose a Contrastive Joint Embedding approach for Multiple Networks, CJEMN, that employs negative sampling and pseudo-labeling to obtain a meaningful embedding of all information within an attributed multiplex network. To the best of our knowledge, this is the first approach that utilizes negative sampling and pseudo-labeling to jointly embed nodes, node attributes, and class labels of attributed multiplex networks in a low-dimensional space. In addition to using spectral embedding and homogeneity analysis, our method incorporates negative pairs as a new layer to enhance the representation of similarities and dissimilarities among nodes, attributes, and class labels. We run experiments on five real-world datasets to evaluate the performance of CJEMN. Our approach outperforms state-of-the-art methods for downstream tasks, such as node classification and clustering.

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.

Datacenter networks have become a critical infrastructure of our digital society and over the last years, great efforts have been made to better understand the communication patterns inside datacenters. In particular, existing empirical studies showed that datacenter traffic typically features much temporal and spatial structure, and that at any given time, some communication pairs interact much more frequently than others. This paper generalizes this study to communication groups and analyzes how clustered the datacenter traffic is, and how stable these clusters are over time. To this end, we propose a methodology which revolves around a biclustering approach, allowing us to identify groups of racks and servers which communicate frequently over the network. In particular, we consider communication patterns occurring in three different Facebook datacenters: a Web cluster consisting of web servers serving web traffic, a Database cluster which mainly consists of MySQL servers, and a Hadoop cluster. Interestingly, we find that in all three clusters, small groups of racks and servers can produce a large fraction of the network traffic, and we can determine these groups even when considering short snapshots of network traffic. We also show empirically that these clusters are fairly stable across time. Our insights on the size and stability of communication clusters hence uncover an interesting potential for resource optimizations in datacenter infrastructures.

Complex information can be represented as networks (graphs) characterized by a large number of nodes, multiple types of nodes, and multiple types of relationships between them, i.e. multiplex networks. Additionally, these networks are enriched with different types of node features.

Graph clustering aims at discovering a natural grouping of the nodes such that similar nodes are assigned to a common cluster. Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes. However, complex data in many domains can be represented as both attributed and multi-relational networks.