Formal description of a generic graph model with RTPA
Bibliographic record
Abstract
Formal specification of abstract data types (ADTs) is important in modeling system architectures and their implementations. One of the most widely used ADTs is graph, since many problems in sciences and engineering can be formulated and solved by a graph model. In this paper, we present a formal approach to the specification of graphs as an ADT using real-time process algebra (RTPA). RTPA is a formal method that describes a software system, especially a real-time system, as a set of processes. We use RTPA to describe a generic graph model in three parts encompassing the architecture, static and dynamic behaviors. In the RTPA specification, the graph behaviors can be classified into four categories, namely: (a) basic operations (InsertNode, DeleteNode, InsertEdge, DeleteEdge, GetSize, GetNumberOfEdges, Retrieve, Update, and Search); (b) node/edge-specific operations (FindNode, FindEdge, Fanin, Fanout, FindNeighbours, and Degree); (c) pointer operations (CurrentNode and CurrentEdge); and (d) utility operations (Create, Clear, and Release). Each of the 20 operations is formally described by an RTPA processes in a unified encapsulation of graph behaviors. On the basis of the RTPA graph model, a wide range of graph-based applications can be implemented by sharing the common architecture and behaviors of the graph as a system type or class.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".