A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PanRep learns universal node embeddings for heterogeneous graphs.
There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs s…
Optimal Transport Graph Neural Networks (OT-GNN) improves graph embeddings by using optimal transport.
Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and …
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approach…
Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node. While most existing approaches need to specify the neighborhood and the dependence form to the neighborhood, which may significantly degrades the flexibility of represent…
Neural Spacetimes learn DAGs by embedding nodes in a spacetime manifold.
Implementing -NN classification using Gromov--Wasserstein distances
PieClam autoencodes graphs into communities, improving graph anomaly detection.
Boost GNNs for node classification by incorporating label dependencies.
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models; however, they can be computationally costly due to significant t…
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the ex…
Paper proves impossibility of three desirable properties in node embedding.
Study finds significant instability in node embeddings due to randomness.
NODEs can approximate a wide range of diffeomorphisms with strong guarantees.
Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respe…
This paper studies node embeddings of networks, revealing their geometric properties.
GNNs with random node initialization are shown to be universally expressive.
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Seq…
SANNE model generates embeddings for unseen nodes in graph networks.
ARGEW improves node embeddings for weighted homophilous graphs by emphasizing strong edge weights.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
New sampling methods improve node embedding efficiency.
DiSeNE generates interpretable node embeddings without supervision.
Paper proposes a novel graph recovery attack from node embeddings.
A new method combines multiple node embeddings using tensor decomposition.
This work provides the first unifying theoretical framework for node (positional) embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogo…
Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data Protection Regulation (GDPR). In this light, we investigate network embeddings, i.e., the representation of network nodes as low-dimensional …
AUASE embeds dynamic networks with stability guarantees for node comparison.
Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning tasks such as node classification, link prediction and community detection, has led to increased interest on the problem leading to a number…
Paper proposes DMGD for integrating outlier and community detection in graph embedding.
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches …
Proposes a novel node embedding framework for graphs using Fisher Information.
Landmark-based node embeddings approximate shortest path distances in random graphs.
In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that cre…
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…
This paper improves spectral embedding for multipartite networks, revealing latent subspaces and providing consistent node representations.
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…
asp2vec learns dynamic node aspect distributions for better network embedding.
New method uses MHN for associative learning in network embedding.
This work analyzes PPR-based node embeddings and their topological information.
DEAL model predicts links for new nodes with only attribute info.
CADE learns dual node representations for better generalization.
The paper examines node2vec embeddings for community detection in networks.
In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representation embeddings. We use an external knowledge base that is organized as a taxonomy of human-understandable concepts over entities as a guide…
This work shows dimension regularization can replace skip-gram negative sampling for graph embeddings, improving efficiency and performance.
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target n…
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactio…