Learning Graph Representations with Embedding Propagation (1710.03059v1)

Abstract: We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes. Backward messages consist of gradients that result from aggregating the label representations and applying a reconstruction loss. Node representations are finally computed from the representation of their labels. With significantly fewer parameters and hyperparameters an instance of EP is competitive with and often outperforms state of the art unsupervised and semi-supervised learning methods on a range of benchmark data sets.

Learning Graph Representations with Embedding Propagation

The paper "Learning Graph Representations with Embedding Propagation" introduces Embedding Propagation (Ep), an unsupervised framework designed to learn vector representations of graph-structured data. This method leverages the inherent connectivity of graphs across various domains, such as social networks and bioinformatics, to improve existing practices in network classification, statistical relational learning, and link prediction. The proposed framework focuses on passing two types of messages—forward and backward—between neighboring nodes to create effective embeddings.

Core Methodology

Ep's fundamental process consists of messaging between nodes to learn embeddings with minimal hyperparameters. Forward messages contain label representations, which can include textual, categorical, or continuous node attributes, while backward messages pass gradients derived from a reconstruction loss back to adjacent nodes for updating representations. The vectors learned from these interactions encapsulate the combined information of a node's attributes and its neighbors.

The framework comprises two primary steps:

1. Label Embedding Learning: Ep initially creates an embedding for every label by aggregating information from neighboring nodes, akin to power iteration algorithms. This initial learning serves to create embeddings that encapsulate potentially varied data types, such as text or images.
2. Node Representation: Ultimately, Ep computes the final node representation by consolidating these label embeddings, offering a multi-modal approach to learning node features across different datasets.

With these mechanisms, Ep generalizes several traditional methods and proves effective in unsupervised learning tasks, outperforming established methods while requiring fewer computational resources and hyperparameter tuning.

Comparisons and Experimental Results

Ep’s relationship with existing techniques, such as Graph Neural Networks (GNN) and Graph Convolutional Networks (GCN), is expounded upon in the paper. Unlike these frameworks, Ep operates in an unsupervised manner and is classifier agnostic, focusing solely on reconstructing node representations based on label embeddings. Similarly, the relation to word embedding models like DeepWalk, Line, and Node2vec is discussed. The comparative advantage of Ep in synthesizing both graph attributes and node identities is demonstrated through a series of benchmark evaluations.

In transductive and inductive scenarios, Ep consistently produced superior embedding quality, particularly on datasets encompassing various node attributes. The framework’s transparency to edge direction and multi-relational graphs further underscores its versatility and robustness. Experimental evaluations show that Ep improves clustering quality and reconstruction loss while decreasing training time.

Implications and Future Directions

From a practical standpoint, Ep's efficiency makes it desirable for large-scale graph-structured data applications such as social network analysis, recommendations, fraud detection, and natural language processing tasks. Theoretically, it presents a novel approach to embeddings by consolidating multi-modal data sources within single node representations.

Future research directions include exploring Ep's integration with multitask learning paradigms, leveraging more complex data types such as sequences and images, and its application in distributed graph processing contexts. Additionally, expanding Ep’s functionality to more diverse graph types, particularly multi-relational graphs, poses an intriguing potential for research and development. The promise of multi-modal and inductive capabilities establishes Ep as a capable framework for addressing evolving challenges in the landscape of machine learning and networked data representation.