Guest Editorial
Bibliographic record
Abstract
Modeling and analyzing networks is a major emerging topic in different research areas, such as computational biology, social science, document retrieval and social web applications.By connecting objects, it is possible to obtain an intuitive and global view of the relationships among components of a complex system.Nowadays, scientific communities have access to huge volume of network-structured data, such as social networks, gene/proteins/metabolic networks, sensor networks, and peer-to-peer networks.Often, data is collected at different time points allowing capturing a dynamic trend of the observed network.Consequently, the time component plays a key role in the comprehension of the evolutionary behavior of the studied network (evolution of the network structure and/or of flows within the system).Time can help to determine the real causal relationships within, for instance, gene activations, link creation, and information flow.Handling such data is a major challenge for current research in machine learning and data mining, and it has led to the development of recent innovative techniques that consider complex/multi-level networks, time-evolving graphs, heterogeneous information (nodes and links), and requires scalable algorithms that are able to manage large-scale complex networks.This special issue is the follow-up of the Dynamic Networks and Knowledge Discovery workshop (DyNaK) 1 that has been held in conjunction to ECML-PKDD 2011 at Barcelona on September 24th 2011.The workshop was motivated by the interest of providing a meeting point for scientists with different backgrounds who are interested in the study of large-scale dynamic complex networks.The workshop has attracted 18 submissions out of which 9 papers has been accepted.The workshop has gathered more than 30 participants and was also the host of three highly appreciated invited keynotes and one industrial talk.Building on the success of the DyNaK workshop, an open call for papers has been issued for this special issue, focusing on the major topic discussed in the workshop: analyzing, modeling and mining large-scale real network.15 high quality papers have been received; each of which has been reviewed by three reviewers.Only 7 contributions were finally selected.These contributions show the vitality of the field: a broad panel of techniques are applied to modeling the dynamics of complex systems, using a wide set of formalisms ranging from descriptive rules to Probabilistic Real-Time Automata.Application fields are also wide: vision, opinion diffusion in social network, business process modeling and text mining.In Internal link prediction: a new approach for predicting links in bipartite graphs, Allali et al. present an algorithm for predicting internal link in bipartite graph.They address the problem of predicting
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.399 | 0.258 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".