Topic
Multiple time dimensions
About: Multiple time dimensions is a research topic. Over the lifetime, 215 publications have been published within this topic receiving 3600 citations.
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TL;DR: It is argued that research in the entrepreneurial context could benefit by providing a theorybased rationale for examining the given dimension(s), including multiple dimensions of performance where possible, and including consideration of several critical control variables such as industry, age, and size of the firm.
1,095 citations
TL;DR: In this paper, the authors argue that time can and should play a more important role because it can change the ontological description and meaning of a theoretical construct and of the relationships between constructs.
555 citations
TL;DR: A schema is proposed to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks and suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.
Abstract: Social media analytics has become prominent in natural disaster management. In spite of a large variety of metadata fields in social media data, four dimensions i.e. space, time, content and network have been given particular attention for mining useful information to gain situational awareness and improve disaster response. In this article, we review how existing studies analyze these four dimensions, summarize common techniques for mining these dimensions, and then suggest some methods accordingly. We then propose a schema to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks. We find that 1 a large part of studies involve multiple dimensions of social media data in their analyses, 2 there are both separate analyses for each dimension and simultaneous analyses for multiple dimensions and 3 there are fewer simultaneous analyses as dimensions increase. Finally, we suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.
176 citations
Proceedings Article•
12 Oct 1981
93 citations
02 Feb 2018
TL;DR: This paper provides an approach to capture independent information from each dimension and dependent information across dimensions and proposes a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure.
Abstract: Information networks are ubiquitous in many applications. A popular way to facilitate the information in a network is to embed the network structure into low-dimension spaces where each node is represented as a vector. The learned representations have been proven to advance various network analysis tasks such as link prediction and node classification. The majority of existing embedding algorithms are designed for the networks with one type of nodes and one dimension of relations among nodes. However, many networks in the real-world complex systems have multiple types of nodes and multiple dimensions of relations. For example, an e-commerce network can have users and items, and items can be viewed or purchased by users, corresponding to two dimensions of relations. In addition, some types of nodes can present hierarchical structure. For example, authors in publication networks are associated to affiliations; and items in e-commerce networks belong to categories. Most of existing methods cannot be naturally applicable to these networks. In this paper, we aim to learn representations for networks with multiple dimensions and hierarchical structure. In particular, we provide an approach to capture independent information from each dimension and dependent information across dimensions and propose a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure. Experimental results on a network from a real-world e-commerce website demonstrate the effectiveness of the proposed framework.
90 citations