Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

/pdf/link-prediction-in-complex-networks-a-survey-fcztp3ymq3.pdf

Link prediction in complex networks: A survey

인공고관절의 세라믹 볼 헤드의 기계적 안정성 평가를 위한 3 차원 유한요소 해석

https://people.mpi-inf.mpg.de/~kberberi/publications/2010-mpii-tra.pdf

YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia

https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

Ranking methods like PageRank assess the importance of Web pages based on the current state of the rapidly evolving Web graph. The dynamics of the resulting importance scores, however, have not been considered yet, although they provide the key to an understanding of the Zeitgeist on the Web. This paper proposes the BuzzRank method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance scores. We experimentally demonstrate the usefulness of BuzzRank on a bibliographic dataset.

BuzzRank ... and the Trend is Your Friend

Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biological systems, and many more. They necessitate research beyond the work related to static graphs in terms of their generative modeling and representation learning. In this survey, we comprehensively review the neural time dependent graph representation learning and generative modeling approaches proposed in recent times for handling temporal graphs. Finally, we identify the weaknesses of existing approaches and discuss the research proposal of our recently published paper TIGGER[24].

A Survey on Temporal Graph Representation Learning and Generative Modeling

The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in many RDF processing engines. In addition to large repositories of geo-spatial data -- e.g., LinkedGeoData, OpenStreetMap, etc. -- spatial data is also routinely found in automatically constructed knowledgebases such as Yago and WikiData. While there have been research efforts for efficient processing of spatial data in RDF/SPARQL, very little effort has gone into building end-to-end systems that can holistically handle complex SPARQL queries along with spatial filters. 
In this paper, we present Streak, a RDF data management system that is designed to support a wide-range of queries with spatial filters including complex joins, top-k, higher-order relationships over spatially enriched databases. Streak introduces various novel features such as a careful identifier encoding strategy for spatial and non-spatial entities, the use of a semantics-aware Quad-tree index that allows for early-termination and a clever use of adaptive query processing with zero plan-switch cost. We show that Streak can scale to some of the largest publicly available semantic data resources such as Yago3 and LinkedGeoData which contain spatial entities and quantifiable predicates useful for result ranking. For experimental evaluations, we focus on top-k distance join queries and demonstrate that Streak outperforms popular spatial join algorithms as well as state of the art end-to-end systems like Virtuoso and PostgreSQL.

STREAK: An Efficient Engine for Processing Top-k SPARQL Queries with Spatial Filters.

Given two sets of entities - potentially the results of two queries on a knowledge-graph like YAGO or DBpedia- characterizing the relationship between these sets in the form of important people, events and organizations is an analytics task useful in many domains. In this paper, we present an intuitive and efficiently computable vertex centrality measure that captures the importance of a node with respect to the explanation of the relationship between the pair of query sets. Using a weighted link graph of entities contained in the English Wikipedia, we demonstrate the usefulness of the proposed measure.

http://ceur-ws.org/Vol-1035/iswc2013_poster_22.pdf

Efficient computation of relationship-centrality in large entity-relationship graphs

This paper pursues the recently emerging paradigm of searching for entities that are embedded in Web pages. We utilize informationextraction techniques to identify entity candidates in documents, map them onto entries in a richly structured ontology, and derive a generalized data graph that encompasses Web pages, entities, and ontological concepts and relationships. We exploit this combination of pages and entities for a novel kind of search-result ranking, coined EntityAuthority, in order to improve the quality of keyword queries that return either pages or entities. To this end, we utilize the mutual reinforcement between authoritative pages and important entities. This resembles the HITS method for Web-graph link analysis and recently proposed ObjectRank methods, but our approach operates on a much richer, typed graph structure with different kinds of nodes and also differs in the underlying mathematical denitions. Preliminary experiments with topic-specic slices of Wikipedia demonstrate the effectiveness of our approach on certain classes of queries.

Srikanta Bedathur

Papers

BuzzRank ... and the Trend is Your Friend

A Survey on Temporal Graph Representation Learning and Generative Modeling

STREAK: An Efficient Engine for Processing Top-k SPARQL Queries with Spatial Filters.

Efficient computation of relationship-centrality in large entity-relationship graphs

EntityAuthority: Semantically Enriched Graph-Based Authority Propagation