Using Bi-partite graphs to cluster complex networks
Bibliographic record
Abstract
The Internet of Things (IoT) has been well established as the next major innovation in the internet and connected devices. The IoT will consist of static sensors, sensors that remain in a fixed location, as well as mobile sensors, sensors that are in motion during their operation. These IoT devices will have to communicate with one another in order to achieve a common goal, and make smart decisions. How these complex networks of devices determine which other devices they will communicate with is the problem addressed in this paper. The methodology used here consists of three major components; the construction of mobility neighborhoods, the utilization of bipartite graphs to represent the network, and the clustering of the bi-partite graph using the Louvain community detection algorithm to partition the network into communities of high modularity. This methodology is implemented using a dataset based on vehicle trajectories on a 600 meter strip of Highway 101 in the United States. The preliminary results show that the methodology can be used to find clusters of vehicles with a high modularity along the strip of highway. These results are preliminary and case specific to the vehicles on the highway, however the general methodology could be applied to any network of IoT devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".