â â â  â â â â â  abstract â  the research on social networks and collaborative strategies has highlighted from the mid of 1980 which has contributed to the success and development of firms. the relationship and communication with trade partners in overseas help success of firms in entering to foreign markets and improve new partners and new markets abroad. doing firm internationalization in fo...

Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models (PRMs) embraces the...

An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Because of the existence of the intermediate measures connecting the supply chain members, conventional data envelopment analysis (DEA) cannot be utilized directly to measure the performance of supply chain and its members. This paper evaluated the performance of supply chai...

The theoretical study of the relational model of data is ongoing and highly developed. Yet the vast majority of real databases include incomplete data, and the incomplete data is widely modelled using special values called nulls. As noted many times by Date and others, the inclusion of special values is not compatible with the relational model and invalidates many of the theoretical results. In...

Relations are fundamental structures for knowledge representation. Relational queries are used to extract information from associated relations in databases. We propose to include relations in constraint programming (CP) as decision variables and promoting relational operations to constraints. That leads to new possibilities for modeling and solving CSPs. Performance and applicability are two a...

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of models, relational can represent and reason about complex dynamic systems exhibiting such feedback loops. Existing cyclic discovery algorithms for learning models from observational data assume...

Reynolds’ theory of relational parametricity formalizes parametric polymorphism for System F, thus capturing the idea that polymorphically typed System F programs always map related inputs to related results. This paper shows that Reynolds’ theory can be seen as the instantiation at dimension 1 of a theory of relational parametricity for System F that holds at all higher dimensions, including i...

In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scor...

Using information about a node’s link, neighborhood, in classification models of node behavior typically improves predictive performance in complex network domains. For example, in social networks, friends can provide additional information about a user’s preferences, attitudes, and behaviors. Recent work in relational learning and social network mining has focused on developing models to explo...