Exploring Communities in Large Profiled Graphs

Given a graph $G$ and a vertex $q\in G$, the community search (CS) problem aims to efficiently find a subgraph of $G$ whose vertices are closely related to $q$. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitate efficient and online solutions for PCS

An Elman Model Based on GMDH Algorithm for Exchange Rate Forecasting

Since the Elman Neural Networks was proposed, it has attracted wide attention. This method has fast convergence and high prediction accuracy. In this study, a new hybrid model that combines the Elman Neural Networks and the group method of data handling (GMDH) is used to forecast the exchange rate. The GMDH algorithm is used for system modeling. Input variables are selected by the external standards. Based on the output of the GMDH algorithm, valid input variables can be used as an input for the Elman Neural Networks for time series prediction. The empirical results show that the new hybrid algorithm is a useful tool.

Location optimization of biodiesel processing plant based on rough set and clustering algorithm - a case study in China

Biofuel has an important role in alleviating the environmental pollution problem. More attention has been paid to optimization of biofuel supply chain in recent years. In this paper, a scientific, rational and practical biodiesel processing plant location with waste oil as the raw material was proposed in order to provide a theoretical basis for guiding the planning and management of restaurants, waste oil collection points, and processing plants. Considering the merits and demerits of the subjective and objective weighting methods, this paper proposes a new weighting method which is namely the combination of rough set theory and clustering algorithm. It then verifies the location results with a plant carbon emission. At last, this paper analyzes the location of biodiesel processing plant in the Yangtze River Delta of China and finds that the precision has been greatly improved with the new method comparing the RMSE and the R2 of the Delphi method with the improved rough set theory. By using this method, the weights of the influencing factors of biodiesel processing plants are the following: Waste oil supply 0.143, Fixed construction cost factor 0.343, Biodiesel demand 0.143 and Location convenience 0.371. In the comparison between the robust optimization method and the improved rough set theory, it was found that the final location results are the same, all being Jiaxing City. However, the improved rough set theory is much simpler than the robust optimization algorithm in the calculation process

EPISTEMIC MOTIVATION AND KNOWLEDGE CONTRIBUTION BEHAVIORS IN WIKI TEAMS: A CROSS-LEVEL MODERATION MODEL

Prior research on how to facilitate individuals’ participation in wiki knowledge contribution generally pays little attention to the differentiation of knowledge contributions and the embeddedness of individual team members in team context. This paper examines how an individual’s epistemic motivation and team task reflexivity interact to jointly influence adding, deleting and revising behaviors in distinct ways. Empirical data of 166 university students in 51 teams support our hypotheses. Individuals’ adding, deleting and revising behaviors on wikis are influenced differently by the interactive effect of individual epistemic motivation and team task reflexivity. First, the positive relationship between epistemic motivation and adding behaviors is stronger when the team’s task reflexivity is high. Second, the epistemic motivation stimulates deleting behaviors only when team task reflexivity is high. Third, epistemic motivation is significantly associated with more revising behaviors no matter the level of task reflexivity is high or low

Wiki-induced Cognitive Elaboration in Project Teams: An Empirical Study

Researchers have exerted increasing efforts to understand how wikis can be used to improve team performance. Previous studies have mainly focused on the effect of the quantity of wiki use on performance in wiki-based communities; however, only inconclusive results have been obtained. Our study focuses on the quality of wiki use in a team context. We develop a construct of wiki-induced cognitive elaboration, and explore its nomological network in the team context. Integrating the literatures on wiki and distributed cognition, we propose that wiki-induced cognitive elaboration influences team performance through knowledge integration among team members. We also identify its team-based antecedents, including task involvement, critical norm, task reflexivity, time pressure and process accountability, by drawing on the motivated information processing literature. The research model is empirically tested using multiple-source survey data collected from 46 wiki-based student project teams. The theoretical and practical implications of our findings are also discussed

The Communication Complexity of Set Intersection and Multiple Equality Testing

In this paper we explore fundamental problems in randomized communication complexity such as computing Set Intersection on sets of size $k$ and Equality Testing between vectors of length $k$. Sa\u{g}lam and Tardos and Brody et al. showed that for these types of problems, one can achieve optimal communication volume of $O(k)$ bits, with a randomized protocol that takes $O(\log^* k)$ rounds. Aside from rounds and communication volume, there is a \emph{third} parameter of interest, namely the \emph{error probability} $p_{\mathrm{err}}$. It is straightforward to show that protocols for Set Intersection or Equality Testing need to send $\Omega(k + \log p_{\mathrm{err}}^{-1})$ bits. Is it possible to simultaneously achieve optimality in all three parameters, namely $O(k + \log p_{\mathrm{err}}^{-1})$ communication and $O(\log^* k)$ rounds? In this paper we prove that there is no universally optimal algorithm, and complement the existing round-communication tradeoffs with a new tradeoff between rounds, communication, and probability of error. In particular: 1. Any protocol for solving Multiple Equality Testing in $r$ rounds with failure probability $2^{-E}$ has communication volume $\Omega(Ek^{1/r})$. 2. There exists a protocol for solving Multiple Equality Testing in $r + \log^*(k/E)$ rounds with $O(k + rEk^{1/r})$ communication, thereby essentially matching our lower bound and that of Sa\u{g}lam and Tardos. Our original motivation for considering $p_{\mathrm{err}}$ as an independent parameter came from the problem of enumerating triangles in distributed ($\textsf{CONGEST}$) networks having maximum degree $\Delta$. We prove that this problem can be solved in $O(\Delta/\log n + \log\log \Delta)$ time with high probability $1-1/\operatorname{poly}(n)$.Comment: 44 page