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

Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems. In this article, we present Axolotl (Automated crossLocation-network Transfer Learning), a novel method aimed at transferring location preference models learned in a data-rich region to significantly boost the quality of recommendations in a data-scarce region. Axolotl predominantly deploys two channels for information transfer: (1) a meta-learning based procedure learned using location recommendation as well as social predictions, and (2) a lightweight unsupervised cluster-based transfer across users and locations with similar preferences. Both of these work together synergistically to achieve improved accuracy of recommendations in data-scarce regions without any prerequisite of overlapping users and with minimal fine-tuning. We build Axolotl on top of a twin graph-attention neural network model used for capturing the user- and location-conditioned influences in a user-mobility graph for each region. We conduct extensive experiments on 12 user mobility datasets across the US, Japan, and Germany, using three as source regions and nine of them (that have much sparsely recorded mobility data) as target regions. Empirically, we show that Axolotl achieves up to 18% better recommendation performance than the existing state-of-the-art methods across all metrics.

/pdf/doing-more-with-less-overcoming-data-scarcity-for-poi-7kurvqfs.pdf

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction, and so forth. In this article, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a framework called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a user correlation model that uses an exponential distribution. We further develop LoCaTe+, an advanced model within the same framework where user correlation is quantified using a Mutually Exciting Hawkes Process. We show the applicability of LoCaTe and LoCaTe+ for location promotion and location recommendation tasks using LBSNs. Our models are validated using a long-term crawl of Foursquare data collected between January 2015 and February 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of the LoCaTe framework in capturing location-specific influence between users. We also show that our models improve over state-of-the-art models for the task of location promotion as well as location recommendation.

/pdf/location-specific-influence-quantification-in-location-based-16lr7grqzu.pdf

Location-Specific Influence Quantification in Location-Based Social Networks

The recently proposed paradigm of learned Bloom filters (LBF) seems to offer significant advantages over traditional Bloom filters in terms of low memory footprint and overall performance as evidenced by empirical evaluations over static data. Its behavior in presence of updates to the set of keys being stored in Bloom filters is not very well understood. At the same time, maintaining the false positive rates (FPR) of traditional Bloom filters in presence of dynamics has been studied and extensions to carefully expand memory footprint of the filters without sacrificing FPR have been proposed. Building on these, we propose two distinct approaches for handling data updates encountered in practical uses of LBF: (i) CA-LBF, where we adjust the learned model (e.g., by retraining) to accommodate the new "unseen" data, resulting in classifier adaptive methods, and (ii) IA-LBF, where we replace the traditional Bloom filter with its adaptive version while keeping the learned model unchanged, leading to an index adaptive method. In this paper, we explore these two approaches in detail under incremental workloads, evaluating them in terms of their adaptability, memory footprint and false positive rates. Our empirical results using a variety of datasets and learned models of varying complexity show that our proposed methods' ability to handle incremental updates is quite robust.

Adaptive Learned Bloom Filters under Incremental Workloads

There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is at a nascent stage with significant scope for improvement. First, existing generative models do not scale with either the time horizon or the number of nodes. Second, existing techniques are transductive in nature and thus do not facilitate knowledge transfer. Finally, due to relying on one-to-one node mapping from source to the generated graph, existing models leak node identity information and do not allow up-scaling/down-scaling the source graph size. In this paper, we bridge these gaps with a novel generative model called TIGGER. TIGGER derives its power through a combination of temporal point processes with auto-regressive modeling enabling both transductive and inductive variants. Through extensive experiments on real datasets, we establish TIGGER generates graphs of superior fidelity, while also being up to 3 orders of magnitude faster than the state-of-the-art.

/pdf/tigger-scalable-generative-modelling-for-temporal-2z1gd590.pdf

TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

Phrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applications such as entity-oriented search, text analytics, and plagiarism detection. Processing phrase queries is costly, though, since positional information has to be kept in the index and all words, including stopwords, need to be considered. We consider an augmented inverted index that indexes selected variable-length multi-word sequences in addition to single words. We study how arbitrary phrase queries can be processed efficiently on such an augmented inverted index. We show that the underlying optimization problem is NP-hard in the general case and describe an exact exponential algorithm and an approximation algorithm to its solution. Experiments on ClueWeb09 and The New York Times with different real-world query workloads examine the practical performance of our methods.

/pdf/phrase-query-optimization-on-inverted-indexes-4p5kljk5j8.pdf

Srikanta Bedathur

Papers

Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer

Location-Specific Influence Quantification in Location-Based Social Networks

Adaptive Learned Bloom Filters under Incremental Workloads

TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

Phrase Query Optimization on Inverted Indexes