20 research outputs found
Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
Knowledge graphs represent known facts using triplets. While existing
knowledge graph embedding methods only consider the connections between
entities, we propose considering the relationships between triplets. For
example, let us consider two triplets and where is
(Academy_Awards, Nominates, Avatar) and is (Avatar, Wins,
Academy_Awards). Given these two base-level triplets, we see that is a
prerequisite for . In this paper, we define a higher-level triplet to
represent a relationship between triplets, e.g., ,
PrerequisiteFor, where PrerequisiteFor is a higher-level relation.
We define a bi-level knowledge graph that consists of the base-level and the
higher-level triplets. We also propose a data augmentation strategy based on
the random walks on the bi-level knowledge graph to augment plausible triplets.
Our model called BiVE learns embeddings by taking into account the structures
of the base-level and the higher-level triplets, with additional consideration
of the augmented triplets. We propose two new tasks: triplet prediction and
conditional link prediction. Given a triplet and a higher-level relation,
the triplet prediction predicts a triplet that is likely to be connected to
by the higher-level relation, e.g., , PrerequisiteFor,
?. The conditional link prediction predicts a missing entity in a
triplet conditioned on another triplet, e.g., , PrerequisiteFor,
(Avatar, Wins, ?). Experimental results show that BiVE significantly
outperforms all other methods in the two new tasks and the typical base-level
link prediction in real-world bi-level knowledge graphs.Comment: 14 pages, 3 figures, 15 tables. 37th AAAI Conference on Artificial
Intelligence (AAAI 2023
Recommended from our members
Overlapping community detection in massive social networks
Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex networks. A community is a set of cohesive vertices that has more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this thesis, we propose scalable overlapping community detection algorithms that effectively identify high quality overlapping communities in various real-world networks.
We first develop an efficient overlapping community detection algorithm using a seed set expansion approach. The key idea of this algorithm is to find good seeds and then greedily expand these seeds using a personalized PageRank clustering scheme. Experimental results show that our algorithm significantly outperforms other state-of-the-art overlapping community detection methods in terms of run time, cohesiveness of communities, and ground-truth accuracy.
To develop more principled methods, we formulate the overlapping community detection problem as a non-exhaustive, overlapping graph clustering problem where clusters are allowed to overlap with each other, and some nodes are allowed to be outside of any cluster. To tackle this non-exhaustive, overlapping clustering problem, we propose a simple and intuitive objective function that captures the issues of overlap and non-exhaustiveness in a unified manner. To optimize the objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using a low-rank semidefinite programming technique. Our experimental results show that the new objective and the algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness.
We extend our non-exhaustive, overlapping clustering techniques to co-clustering where the goal is to simultaneously identify a clustering of the rows as well as the columns of a data matrix. As an example application, consider recommender systems where users have ratings on items. This can be represented by a bipartite graph where users and items are denoted by two different types of nodes, and the ratings are denoted by weighted edges between the users and the items. In this case, co-clustering would be a simultaneous clustering of users and items. We propose a new co-clustering objective function and an efficient co-clustering algorithm that is able to identify overlapping clusters as well as outliers on both types of the nodes in the bipartite graph. We show that our co-clustering algorithm is able to effectively capture the underlying co-clustering structure of the data, which results in boosting the performance of a standard one-dimensional clustering.
Finally, we study the design of parallel data-driven algorithms, which enables us to further increase the scalability of our overlapping community detection algorithms. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve a significantly superior scalability than standard PageRank implementations. The design choices affect both single-threaded performance as well as parallel scalability. The lessons learned from this study not only guide efficient implementations of many graph mining algorithms but also provide a framework for designing new scalable algorithms, especially for large-scale community detection.Computer Science
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Inductive knowledge graph completion has been considered as the task of
predicting missing triplets between new entities that are not observed during
training. While most inductive knowledge graph completion methods assume that
all entities can be new, they do not allow new relations to appear at inference
time. This restriction prohibits the existing methods from appropriately
handling real-world knowledge graphs where new entities accompany new
relations. In this paper, we propose an INductive knowledge GRAph eMbedding
method, InGram, that can generate embeddings of new relations as well as new
entities at inference time. Given a knowledge graph, we define a relation graph
as a weighted graph consisting of relations and the affinity weights between
them. Based on the relation graph and the original knowledge graph, InGram
learns how to aggregate neighboring embeddings to generate relation and entity
embeddings using an attention mechanism. Experimental results show that InGram
outperforms 14 different state-of-the-art methods on varied inductive learning
scenarios.Comment: 14 pages, 4 figures, 6 tables, 40th International Conference on
Machine Learning (ICML 2023
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
A hyper-relational knowledge graph has been recently studied where a triplet
is associated with a set of qualifiers; a qualifier is composed of a relation
and an entity, providing auxiliary information for a triplet. While existing
hyper-relational knowledge graph embedding methods assume that the entities are
discrete objects, some information should be represented using numeric values,
e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford
Univ.) can be associated with a qualifier such as (start time, 1911). In this
paper, we propose a unified framework named HyNT that learns representations of
a hyper-relational knowledge graph containing numeric literals in either
triplets or qualifiers. We define a context transformer and a prediction
transformer to learn the representations based not only on the correlations
between a triplet and its qualifiers but also on the numeric information. By
learning compact representations of triplets and qualifiers and feeding them
into the transformers, we reduce the computation cost of using transformers.
Using HyNT, we can predict missing numeric values in addition to missing
entities or relations in a hyper-relational knowledge graph. Experimental
results show that HyNT significantly outperforms state-of-the-art methods on
real-world datasets.Comment: 11 pages, 5 figures, 12 tables. 29th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2023
HiddenCPG: Large-Scale Vulnerable Clone Detection Using Subgraph Isomorphism of Code Property Graphs
Scalable and Memory-Efficient Clustering of Large-Scale Social Networks
Abstract—Clustering of social networks is an important task for their analysis; however, most existing algorithms do not scale to the massive size of today’s social networks. A popular class of graph clustering algorithms for large-scale networks, such as PMetis, KMetis and Graclus, is based on a multilevel framework. Generally, these multilevel algorithms work reasonably well on networks with a few million vertices. However, when the network size increases to the scale of 10 million vertices or greater, the performance of these algorithms rapidly degrades. Furthermore, an inherent property of social networks, the power law degree distribution, makes these algorithms infeasible to apply to large-scale social networks. In this paper, we propose a scalable and memory-efficient clustering algorithm for large-scale social networks. We name our algorithm GEM, by mixing two key concepts of the algorithm, Graph Extraction and weighted kernel k-Means. GEM efficiently extracts a good skeleton graph from the original graph, and propagates the clustering result of the extracted graph to the rest of the network. Experimental results show that GEM produces clusters of quality comparable to or better than existing state-of-the-art graph clustering algorithms, while it is much faster and consumes much less memory. Furthermore, the parallel implementation of GEM, called PGEM, not only produces higher quality of clusters but also achieves much better scalability than most current parallel graph clustering algorithms. Keywords-clustering; social networks; graph clustering; scalable computing; graph partitioning; kernel k-means; I
Overlapping Community Detection Using Seed Set Expansion
Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. One of the most successful techniques for finding overlapping communities is based on local optimization and expansion of a community metric around a seed set of vertices. In this paper, we propose an efficient overlapping community detection algorithm using a seed set expansion approach. In particular, we develop new seeding strategies for a personalized PageRank scheme that optimizes the conductance community score. The key idea of our algorithm is to find good seeds, and then expand these seed sets using the personalized PageRank clustering procedure. Experimental results show that this seed set expansion approach outperforms other state-of-the-art overlapping community detection methods. We also show that our new seeding strategies are better than previous strategies, and are thus effective in finding good overlapping clusters in a graph