3 research outputs found
Adversarial Permutation Guided Node Representations for Link Prediction
After observing a snapshot of a social network, a link prediction (LP)
algorithm identifies node pairs between which new edges will likely materialize
in future. Most LP algorithms estimate a score for currently non-neighboring
node pairs, and rank them by this score. Recent LP systems compute this score
by comparing dense, low dimensional vector representations of nodes. Graph
neural networks (GNNs), in particular graph convolutional networks (GCNs), are
popular examples. For two nodes to be meaningfully compared, their embeddings
should be indifferent to reordering of their neighbors. GNNs typically use
simple, symmetric set aggregators to ensure this property, but this design
decision has been shown to produce representations with limited expressive
power. Sequence encoders are more expressive, but are permutation sensitive by
design. Recent efforts to overcome this dilemma turn out to be unsatisfactory
for LP tasks. In response, we propose PermGNN, which aggregates neighbor
features using a recurrent, order-sensitive aggregator and directly minimizes
an LP loss while it is `attacked' by adversarial generator of neighbor
permutations. By design, PermGNN{} has more expressive power compared to
earlier symmetric aggregators. Next, we devise an optimization framework to map
PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds
up reporting the top- most likely edges for the LP task. Our experiments on
diverse datasets show that \our outperforms several state-of-the-art link
predictors by a significant margin, and can predict the most likely edges fast.Comment: Rectified an error in evaluation in earlier 60-40 split
Interpretable Neural Subgraph Matching for Graph Retrieval
Given a query graph and a database of corpus graphs, a graph retrieval system aims to deliver the most relevant corpus graphs. Graph retrieval based on subgraph matching has a wide variety of applications, e.g., molecular fingerprint detection, circuit design, software analysis, and question answering. In such applications, a corpus graph is relevant to a query graph, if the query graph is (perfectly or approximately) a subgraph of the corpus graph. Existing neural graph retrieval models compare the node or graph embeddings of the query-corpus pairs, to compute the relevance scores between them. However, such models may not provide edge consistency between the query and corpus graphs. Moreover, they predominantly use symmetric relevance scores, which are not appropriate in the context of subgraph matching, since the underlying relevance score in subgraph search should be measured using the partial order induced by subgraph-supergraph relationship. Consequently, they show poor retrieval performance in the context of subgraph matching. In response, we propose ISONET, a novel interpretable neural edge alignment formulation, which is better able to learn the edge-consistent mapping necessary for subgraph matching. ISONET incorporates a new scoring mechanism which enforces an asymmetric relevance score, specifically tailored to subgraph matching. ISONET’s design enables it to directly identify the underlying subgraph in a corpus graph, which is relevant to the given query graph. Our experiments on diverse datasets show that ISONET outperforms recent graph retrieval formulations and systems. Additionally, ISONET can provide interpretable alignments between query-corpus graph pairs during inference, despite being trained only using binary relevance labels of whole graphs during training, without any fine-grained ground truth information about node or edge alignments