An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

An Examination of the Success of Post-merger IT Integration

IT integration is one of the most critical and complex tasks in a merger-and-acquisition (M&A) project. Many studies on M&A have focused on the technological and organizational issues of IT integration but have left out the important base of what to measure for the success of the integration project. Existing researches remain sparse in explaining the assessment of the performance of a post-merger IT integration. The success of IT integration from merging with other companies can be viewed differently by different stakeholders such as the CEOs, the CIOs, the business managers, and the customers of the merged companies, and therefore the standards used vary from process enhancement to customer satisfaction. The objective of this research is to examine the success of post-merger IT integration from both strategic and operational viewpoints and to track the integration performance using a wide range of business measures. It is hoped that the resultant measurement of IT integration can provide a consistent and useful benchmark for evaluating the integration success with IT after business investment in M&A projects

Measuring smile curves in global value chains

The concept and logic of the "smile curve" in the context of global value chains has been widely used and discussed at the individual firm level, but rarely identified and investigated at the country and industry levels by using real data. This paper proposes an idea, based on an inter-country input-output model, to consistently measure both the strength and length of linkages between producers and consumers along global value chains. This idea allows for better identification and mapping of smile curves for countries and industries according to their positions and degrees of participation in a given conceptual value chain. Using the 1995-2011 World Input-Output Tables, several conceptual value chains are investigated, including exports of electrical and optical equipment from China and Mexico and exports of automobiles from Japan and Germany. The identified smile curves provide a very intuitive and visual image, which can significantly improve our understanding of the roles played by different countries and industries in global value chains. Further, the smile curves help identify the benefits gained by these countries and industries through their participation in global trade