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A short introduction to probabilistic soft logic
TLDR
This paper provides an overview of the PSL language and its techniques for inference and weight learning. Expand
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Probabilistic Similarity Logic
TLDR
This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates relational structure about similarities and relational structure in a principled way. Expand
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DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases
TLDR
In this paper, we first propose the DOGMA index for fast subgraph matching on disk and then develop a basic algorithm to answer queries over this index. Expand
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A Scalable Framework for Modeling Competitive Diffusion in Social Networks
TLDR
We introduce the weighted generalized annotated program (wGAP) framework that allows for the modeling of competing diffusion processes. Expand
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COSI: Cloud Oriented Subgraph Identification in Massive Social Networks
TLDR
We use cloud computing in conjunction with existing single processor methods to efficiently match complex subgraphs on graphs as large as 778M edges. Expand
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Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
TLDR
We improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piece wise-quadratic dependencies and linear constraints over continuous domains. Expand
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Efficient Multiview Maintenance under Insertion in Huge Social Networks
TLDR
We develop a very fast MultiView algorithm that scalably and efficiently maintains multiple subgraph views when insertions are made to the social network database. Expand
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Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
TLDR
We first define a class of problems called Social Network Diffusion Optimization Problems (SNDOPs) and show that the GAP paradigm can be used to express many existing diffusion models. Expand
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Probabilistic Subgraph Matching on Huge Social Networks
TLDR
In this paper, we propose "probabilistic subgraph" queries over a graph/network database, which afford users great flexibility in specifying "approximately" what they are looking for. Expand
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A budget-based algorithm for efficient subgraph matching on Huge Networks
As social network and RDF data grow dramatically in size to billions of edges, the ability to scalably answer queries posed over graph datasets becomes increasingly important. In this paper, weExpand
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