We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) 
 
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Thank you for reading victims of groupthink a psychological study of foreign policy decisions and fiascoes. As you may know, people have look hundreds times for their chosen readings like this victims of groupthink a psychological study of foreign policy decisions and fiascoes, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their computer.

Victims Of Groupthink A Psychological Study Of Foreign Policy Decisions And Fiascoes

Creative Cognition: Theory, Research, and Applications

"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

Big data, little data, no data : scholarship in the networkedworld

Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Although the success of online encyclopedias such as Wiki-pedia is indisputable, researchers have questioned usefulness of Wikipedia in educational settings. Problems such as copy&paste syndrome, unchecked quality, or fragmentation of knowledge have been recognized as serious drawbacks for a wide spread application of Wikipedia in universities or high schools. In this paper we present a Wiki-based encyclopedia called Austria-Forum that aims to combine openness and collaboration aspects of Wikipedia with approaches to build a structured, quality inspected, and context-sensitive online encyclopedia. To ensure tractability of the publishing process the system focuses on providing information within a local Austrian context. It is our experience that such an approach represents a first step of a proper application of online encyclopedias in educational settings.

/pdf/the-austrian-way-of-wiki-pedia-development-of-a-structured-30cg4xwu1r.pdf

The Austrian way of Wiki(pedia)!: development of a structured Wiki-based encyclopedia within a local Austrian context

In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics. For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web. Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process. In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.

/pdf/consensus-dynamics-in-online-collaboration-systems-1k1fqp5mqq.pdf

Consensus dynamics in online collaboration systems

Classic resource recommenders like Collaborative Filtering treat users as being just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and interpretation SUSTAIN, as an unsupervised human category learning model, captures these dynamics It aims to mimic a learner’s behavior of categorization In this paper, we use three social bookmarking datasets gathered from BibSonomy, CiteULike and Delicious to investigate SUSTAIN as a user modeling approach to re-rank and enrich Collaborative Filtering following a hybrid recommender strategy Evaluations against baseline algorithms in terms of recommender accuracy and computational complexity reveal encouraging results Our approach substantially improves Collaborative Filtering and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant In a further step, we explore SUSTAIN’s dynamics in our specific learning task and show that both, memorization of a user’s history and clus- tering, contribute to the algorithm’s performance Finally, we observe that the users’ attentional foci determined by SUSTAIN correlate with the users’ level of curiosity, identified by the SPEAR algorithm Overall, the results of our study show that SUSTAIN can be used to efficiently model attention-interpretation dynamics of users and can help to improve Collaborative Filtering in resource recommendation tasks

/pdf/improving-collaborative-filtering-using-a-cognitive-model-of-4y5t8kvbbk.pdf

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Creative group work can be supported by collaborative search and annotation of Web resources In this setting, it is important to help individuals both stay fluent in generating ideas of what to se

https://www.tandfonline.com/doi/pdf/10.1080/10447318.2017.1379240

Ilire Hasani-Mavriqi

Papers

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

The Austrian way of Wiki(pedia)!: development of a structured Wiki-based encyclopedia within a local Austrian context

Consensus dynamics in online collaboration systems

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach