Provable Deterministic Leverage Score Sampling

We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns with probabilities proportional to their leverage scores. In this work, we provide a novel theoretical analysis of deterministic leverage score sampling. We show that such deterministic sampling can be provably as accurate as its randomized counterparts, if the leverage scores follow a moderately steep power-law decay. We support this power-law assumption by providing empirical evidence that such decay laws are abundant in real-world data sets. We then demonstrate empirically the performance of deterministic leverage score sampling, which many times matches or outperforms the state-of-the-art techniques.Comment: 20th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Distributed Representations of Signed Networks

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect polarities inherent in edges. We propose SIGNet, a fast scalable embedding method suitable for signed networks. Our proposed objective function aims to carefully model the social structure implicit in signed networks by reinforcing the principles of social balance theory. Our method builds upon the traditional word2vec family of embedding approaches and adds a new targeted node sampling strategy to maintain structural balance in higher-order neighborhoods. We demonstrate the superiority of SIGNet over state-of-the-art methods proposed for both signed and unsigned networks on several real world datasets from different domains. In particular, SIGNet offers an approach to generate a richer vocabulary of features of signed networks to support representation and reasoning.Comment: Published in PAKDD 201

On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts

Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a non-redundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.Comment: To appear in International Conference on Advances in Social Networks Analysis and Mining (ASONAM '19), Vancouver, BC, Canad

Dynamics of Opinion Forming in Structurally Balanced Social Networks

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends

A Multi-Agent Framework for Personalized Information Filtering

As today the amount of accessible information is overwhelming, the intelligent and personalized filtering of available information is a great challenge. The main problems are that the relevant information is spread over a big number of sources and useful information is hidden under the huge amount of useless data. To cope with this problem several filtering and information query strategies have been developed but they are usually specialized on a bounded problem and do not take into account the individual preferences of the user. Moreover most search engines rate every document separately and do consider the relationship between the documents in the result set. In this paper we present a multi-agent system that integrates heterogeneous information sources, a big number of filtering and rating strategies as well as strategies for combining ratings from different agents and optimizing the filter result set according to the individual user preferences. In the framework each information source, filtering strategy and optimization strategy is presented as an intelligent agent so that the system is open and extendable at runtime. The framework monitors the resource demand of each agent as well as the availability of system resources for choosing the most adequate agents according to the requested response time. User feedback is collected and used for optimizing the filtering strategies and for learning in which context which strategy performs best. The filtering framework provides the basis for the Personalized Information System. The first evaluation results show that the filtering framework provides better results and that new filtering strategies can be seamlessly integrated

This document is under the terms of the CC-BY-NC-ND Creative Commons Attribution A Multi-Agent Framework for Personalized Information Filtering

As today the amount of accessible information is overwhelming, the intelligent and personalized filtering of available information is a great challenge. The main problems are that the relevant information is spread over a big number of sources and useful information is hidden under the huge amount of useless data. To cope with this problem several filtering and information query strategies have been developed but they are usually specialized on a bounded problem and do not take into account the individual preferences of the user. Moreover most search engines rate every document separately and do consider the relationship between the documents in the result set. In this paper we present a multi-agent system that integrates heterogeneous information sources, a big number of filtering and rating strategies as well as strategies for combining ratings from different agents and optimizing the filter result set according to the individual user preferences. In the framework each information source, filtering strategy and optimization strategy is presented as an intelligent agent so that the system is open and extendable at runtime. The framework monitors the resource demand of each agent as well as the availability of system resources for choosing the most adequate agents according to the requested response time. User feedback is collected and used for optimizing the filtering strategies and for learning in which context which strategy performs best. The filtering framework provides the basis for the Personalized Information System. The first evaluation results show that the filtering framework provides better results and that new filtering strategies can be seamlessly integrated.