How to Use the D Core for Graph Mining and Community Detection
The osthread module provides low-level, OS-dependent code for thread creation and management. All threads derive from this class. This function registers the calling thread with the D runtime. Calling this function must be followed by calls to thread_resumeAll.
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Integrated DMFT software for Correlated electrons
As many strongly correlated materials are characterized by quantum impurity effects, it is important to model them with a fully non-perturbative method. This requires a projection/embedding technique that connects the atomic and continuum degrees of freedom of the solid.
This is the basis of the approach developed in this work, which has been embedded into a widely used open source DFT code called Portobello. The method has enabled first-principles study of Mott insulators in both their paramagnetic and antiferromagnetic phases, as well as a narrow-gap correlated semiconductor without any adjustable parameters.
This is the only DFT-based approach that can predict the full spectrum of a spin-1/2 system, as measured by angle-resolved photoemission spectroscopy. This is a vital step towards quantitatively describing the physical properties of highly correlated materials in which DFT breaks down. This approach is currently implemented in several plane-wave DFT codes, including VASP and Abinit. However, there is no dedicated wiki page for the community to share DMFT-based software tools and methods.
Graph-theoretic k-core for directed graphs
The k-core of a graph is a maximal induced subgraph that has vertices with degree at least k. It is an important notion in graph mining because it reveals the higher-order organization of networks. It is used for community detection and evaluation in social networks, as well as in other applications.
Several algorithms have been proposed to find the k-core of a directed graph. However, there is no known bound on the number of minimal k-cores. The enumeration problem is NP-hard.
We present an efficient algorithm based on the Shapley value, which achieves near-optimal performance on some test graphs. It achieves the same performance as the optimal LD algorithm when k is small, but it performs much better when k is large.
We also propose a new algorithm that searches the edge-based k-cores containing a query node q. This search is difficult because the k-core structure can easily collapse after removing the edges connecting to a node.
Community detection and evaluation
Community detection is an important step in network analysis. It allows for the identification of meaningful groups in a graph. Many different methods have been developed for community detection, but their accuracy and computational complexity vary greatly. Many of these methods are based on different assumptions about the structure of communities.
The quality of a community can be evaluated using different metrics, such as the conductance criterion and the F1 score. The conductance metric calculates the number of edges left out of the community compared to the total edge set. It can also be compared to the modularity of a graph, which is a measure of the number of communities that a graph contains.
The accuracies of community detection algorithms depend on the mixing parameter, m, and vary from one algorithm to another. For example, the accuracies of Fastgreedy and Infomap decrease with increasing m. On the other hand, Walktrap and Spinglass have better performance when m is small.
The Wiki graph
A graph, chart or diagram is a representation of data in a visual form. They can be used to convey information efficiently and to present it in a aesthetically pleasing way. They can also be used to compare data sets and identify patterns or trends. The graph extension allows a powerful Vega based graph to be added within wiki pages. It can be edited in a user-friendly dialog, and the raw JSON specification is also editable in classic wikitext if more advanced users wish to tweak settings not supported by the plugin.
The WikiGraphs dataset provides a large set of graph-text paired Wikipedia articles that facilitates research in conditional text generation, graph representation learning and more. Its size and quality are significantly larger than existing graph-text paired datasets. It would be helpful if the authors addressed the question of how to assess performance (e.g. AUC ROC), and how the Wikidata platform compares with other knowledge graph systems.