Latent Representation and Sampling in Network: Application in Text Mining and Biology.

In classical machine learning, hand-designed features are used for learning a mapping from raw data. However, human involvement in feature design makes the process expensive. Representation learning aims to learn abstract features directly from data without direct human involvement. Raw data can be of various forms. Network is one form of data that encodes relational structure in many real-world domains. Therefore, learning abstract features for network units is an important task. In this dissertation, we propose models for incorporating temporal information given as a collection of networks from subsequent time-stamps. The primary objective of our models is to learn a better abstract feature representation of nodes and edges in an evolving network. We show that the temporal information in the abstract feature improves the performance of link prediction task substantially. Besides applying to the network data, we also employ our models to incorporate extra-sentential information in the text domain for learning better representation of sentences. We build a context network of sentences to capture extra-sentential information. This information in abstract feature representation of sentences improves various text-mining tasks substantially over a set of baseline methods. A problem with the abstract features that we learn is that they lack interpretability. In real-life applications on network data, for some tasks, it is crucial to learn interpretable features in the form of graphical structures. For this we need to mine important graphical structures along with their frequency statistics from the input dataset. However, exact algorithms for these tasks are computationally expensive, so scalable algorithms are of urgent need. To overcome this challenge, we provide efficient sampling algorithms for mining higher-order structures from network(s). We show that our sampling-based algorithms are scalable. They are also superior to a set of baseline algorithms in terms of retrieving important graphical sub-structures, and collecting their frequency statistics. Finally, we show that we can use these frequent subgraph statistics and structures as features in various real-life applications. We show one application in biology and another in security. In both cases, we show that the structures and their statistics significantly improve the performance of knowledge discovery tasks in these domains

Spartan Daily, April 7, 2000

Volume 114, Issue 44https://scholarworks.sjsu.edu/spartandaily/9541/thumbnail.jp

Spartan Daily, May 13, 1988

Volume 90, Issue 64https://scholarworks.sjsu.edu/spartandaily/7723/thumbnail.jp

Spartan Daily, May 5, 1997

Volume 108, Issue 65https://scholarworks.sjsu.edu/spartandaily/9140/thumbnail.jp

Prefigurative Post-Politics as Strategy:The Case of Government-Led Blockchain Projects

Critically engaging with literature on post-politics, blockchain and algorithmic governance, and drawing also on knowledge gained from undertaking a three-year empirical study, the purpose of this article is to better understand the transformative capacity of government-led blockchain projects. Analysis of a diversity of empirical material, which was guided by a digital ethnography approach, is used to support the furthering of the existing debate on the nature of the post-political as a condition and/or strategy. Through these theoretical and empirical explorations, the article concludes that while the post-political represents a contingent political strategy by governmental actors, it could potentially impose an algorithmically enforced post-political ‘condition’ for the citizen. It is argued that the design, features and mechanisms of government-led projects are deliberately and strategically used to delimit a citizens’ political agency. In order to address this scenario, we argue that there is a need not only to analyse and contribute to the algorithmic design of blockchain projects (i.e. the affordances and constraints they set), but also to the metapolitical narrative underpinning them (i.e. the political imaginaries underlying the various government-led projects)

Research on the strategy of multinational enterprises: Key approaches and new avenues

Over decades, research on multinational enterprises’ (MNEs) strategies has been anchored in internalization theory. Strongly grounded in transaction cost economics to explain foreign market entry, it hardly explains how MNEs can build and sustain a competitive advantage. Thus, this paper aims at understanding how the nature of strategic thinking has influenced the research in the field of MNEs’ strategy. A content analysis of 1116 papers was conducted. The intellectual structure and dynamics of research to date are provided, without losing sight of the key foundations of strategy and strategic management. The links between human capital and knowledge are the factors on which to underpin the explanation of the MNEs’ strategies and support the coevolving theory. This theory is a promising avenue of research under the umbrella of RBV and KBV approaches. The context-dependency of strategy implies that different contexts require different approaches. Accordingly, we provide insights for future research by combining main schools of strategy though

Spartan Daily, May 11, 1964

Volume 51, Issue 122https://scholarworks.sjsu.edu/spartandaily/4588/thumbnail.jp

Spartan Daily, August 31, 2005

Volume 125, Issue 4https://scholarworks.sjsu.edu/spartandaily/10147/thumbnail.jp