alterlab-sna
De la autor
Applies social-network-analysis method discipline to relational data — degree/betweenness/closeness/eigenvector centrality and PageRank, community detection (Louvain, greedy-modularity, and — from networkx 3.7 — Leiden native in networkx; igraph/leidenalg for large graphs), and inferential network models (ERGM) — choosing the measure that matches the substantive question and the right dependence assumptions, then routing computation to the existing networkx (and igraph/R) tooling. Use when the request mentions social network analysis, centrality, key players/brokerage, community or cluster detection in a network, ERGM, or modeling ties between nodes. For general graph algorithms and plotting prefer alterlab-networkx; for graph neural networks prefer alterlab-torch-geometric. Part of the AlterLab Academic Skills suite.
Instalare
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-sna -a claude-code
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- alterlab-networkxAlterLab-IEU/AlterLab-Academic-SkillsCreates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.Date★ 78
- alterlab-text-as-dataAlterLab-IEU/AlterLab-Academic-SkillsAnalyzes text as social-science data — topic modeling (BERTopic with embeddings + class-based TF-IDF, LDA/NMF via scikit-learn or gensim), document embeddings (sentence-transformers), dictionary/lexicon methods, and supervised text classification — choosing the method that matches the inferential goal (discovery vs measurement vs prediction) and validating topic reliability rather than trusting one stochastic run. It uses the verified stack (BERTopic, scikit-learn, gensim CoherenceModel, spaCy, sentence-transformers) with pinned patterns. Use when the request mentions topic modeling, text as data, computational text analysis, document embeddings, dictionary/sentiment lexicons, or classifying a corpus. For training or fine-tuning transformer models prefer alterlab-transformers; for humanities close-reading corpora prefer alterlab-digital-humanities. Part of the AlterLab Academic Skills suite.Știință★ 78
- alterlab-social-science-methodsAlterLab-IEU/AlterLab-Academic-SkillsGuides advanced social science research methods — discourse analysis (Fairclough CDA, Gee), conversation analysis, quantitative content analysis, Qualitative Comparative Analysis (QCA), process tracing, archival research, participatory/community-based research (PAR, CBPR), Delphi and Q methodology, social network analysis (SNA), bibliometrics and scientometrics, systematic mapping reviews, and program/policy evaluation. Use when designing or conducting any of these studies — choosing a specialized method, building a coding scheme, establishing causal mechanisms in case studies, mapping relational or subjective data, or applying an evaluation framework. For interpretive coding (thematic analysis, grounded theory, IPA, ethnography) use alterlab-qualitative-methods; for qual+quant integration use alterlab-mixed-methods; to run QCA, SNA, or text-as-data analyses on data use alterlab-qca, alterlab-sna, or alterlab-text-as-data. Part of the AlterLab Academic Skills suite.Știință★ 78
- alterlab-torch-geometricAlterLab-IEU/AlterLab-Academic-SkillsGraph Neural Networks with PyTorch Geometric (PyG) — node and graph classification, link prediction, GCN, GAT, and GraphSAGE layers, heterogeneous graphs, and molecular property prediction. Use when building or training GNNs for geometric deep learning on graph-structured data. Part of the AlterLab Academic Skills suite.Altele★ 78
- networkx-graph-analysisjaechang-hits/SciAgent-SkillsGraph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.Altele★ 383
- alterlab-statsmodelsAlterLab-IEU/AlterLab-Academic-SkillsStatistical modeling in Python with statsmodels — OLS/WLS/GLS, GLM, discrete-choice and count models, mixed models, ARIMA/SARIMAX/VAR, with diagnostics, robust standard errors, and coefficient-level inference. Use when fitting specific model classes for econometrics, time series, or rigorous inference with coefficient tables and confidence intervals, or when updating code for statsmodels 0.15 (result_object named results, rng keyword). For guided statistical test selection with APA reporting prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.Testare★ 78
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