为了提供更一致的接口，igraph 函数或参数的一部分正在缓慢但持续地被弃用。您可以在下表中查看当前的弃用级别。
请尽快更新您的代码库，无论是软件包还是脚本。为此，请在安装了最新 igraph 版本的会话中运行您的代码，甚至可以是从 R-universe 安装的开发版本
options(
repos = c(
igraph = 'https://igraph.r-universe.dev',
CRAN = 'https://cloud.r-project.org'
)
)
install.packages('igraph')并注意通过 lifecycle 软件包发出的任何消息。
感谢您的合作！请在 igraph 问题跟踪器中向我们提出任何问题。
| 级别 | 版本 | 旧 | 新 |
|---|---|---|---|
| 1 – 软弃用 | 2.0.0 | graph.adjacency() | graph_from_adjacency_matrix() |
| 1 – 软弃用 | 1.6.0 | graph_from_adjacency_matrix(adjmatrix = ‘必须是矩阵’) | |
| 1 – 软弃用 | 1.6.0 | graph_from_adjacency_matrix(adjmatrix = ‘必须是对称矩阵，且 mode = "undirected"’) | 使用 mode = “max” 以实现原始行为。 |
| 1 – 软弃用 | 1.5.0 | ape::as.phylo() | |
| 1 – 软弃用 | 2.0.0 | assortativity.nominal() | assortativity_nominal() |
| 1 – 软弃用 | 2.0.0 | assortativity.degree() | assortativity_degree() |
| 1 – 软弃用 | 1.6.0 | assortativity(… =) | 参数 values 和 values.in 必须被命名。 |
| 1 – 软弃用 | 1.6.0 | assortativity(types1 =) | assortativity(values =) |
| 1 – 软弃用 | 1.6.0 | assortativity(types2 =) | assortativity(values.in =) |
| 1 – 软弃用 | 2.0.0 | set.vertex.attribute() | set_vertex_attr() |
| 1 – 软弃用 | 2.0.0 | set.graph.attribute() | set_graph_attr() |
| 1 – 软弃用 | 2.0.0 | set.edge.attribute() | set_edge_attr() |
| 1 – 软弃用 | 2.0.0 | remove.vertex.attribute() | delete_vertex_attr() |
| 1 – 软弃用 | 2.0.0 | remove.graph.attribute() | delete_graph_attr() |
| 1 – 软弃用 | 2.0.0 | remove.edge.attribute() | delete_edge_attr() |
| 1 – 软弃用 | 2.0.0 | list.vertex.attributes() | vertex_attr_names() |
| 1 – 软弃用 | 2.0.0 | list.graph.attributes() | graph_attr_names() |
| 1 – 软弃用 | 2.0.0 | list.edge.attributes() | edge_attr_names() |
| 1 – 软弃用 | 2.0.0 | is.weighted() | is_weighted() |
| 1 – 软弃用 | 2.0.0 | is.named() | is_named() |
| 1 – 软弃用 | 2.0.0 | is.bipartite() | is_bipartite() |
| 1 – 软弃用 | 2.0.0 | get.vertex.attribute() | vertex_attr() |
| 1 – 软弃用 | 2.0.0 | get.graph.attribute() | graph_attr() |
| 1 – 软弃用 | 2.0.0 | get.edge.attribute() | edge_attr() |
| 1 – 软弃用 | 2.0.0 | is.igraph() | is_igraph() |
| 3 – 停止使用 | 2.1.0 | get.edge() | ends() |
| 1 – 软弃用 | 2.0.0 | bipartite.projection.size() | bipartite_projection_size() |
| 1 – 软弃用 | 2.0.0 | bipartite.projection() | bipartite_projection() |
| 1 – 软弃用 | 2.0.0 | bipartite.mapping() | bipartite_mapping() |
| 1 – 软弃用 | 2.0.0 | subgraph.centrality() | subgraph_centrality() |
| 1 – 软弃用 | 2.0.0 | page.rank() | page_rank() |
| 2 – 警告 | 2.0.0 | hub.score() | hits_scores() |
| 2 – 警告 | 2.0.0 | authority.score() | hits_scores() |
| 1 – 软弃用 | 2.0.0 | graph.strength() | strength() |
| 1 – 软弃用 | 2.0.0 | graph.eigen() | spectrum() |
| 1 – 软弃用 | 2.0.0 | graph.diversity() | diversity() |
| 1 – 软弃用 | 2.0.0 | evcent() | eigen_centrality() |
| 1 – 软弃用 | 2.0.0 | edge.betweenness() | edge_betweenness() |
| 1 – 软弃用 | 2.0.0 | bonpow() | power_centrality() |
| 1 – 软弃用 | 2.0.0 | alpha.centrality() | alpha_centrality() |
| 1 – 软弃用 | 1.6.0 | estimate_betweenness() | betweenness() |
| 1 – 软弃用 | 1.6.0 | estimate_edge_betweenness() | edge_betweenness() |
| 1 – 软弃用 | 1.6.0 | estimate_closeness() | closeness() |
| 1 – 软弃用 | 1.6.0 | arpack(options = ‘必须是列表’) | c(“arpack_defaults() 现在是一个函数，请使用 options = arpack_defaults() 而不是 options = arpack_defaults。”) |
| 1 – 软弃用 | 1.6.0 | spectrum(options = ‘必须是列表’) | c(“arpack_defaults() 现在是一个函数，请使用 options = arpack_defaults() 而不是 options = arpack_defaults。”) |
| 1 – 软弃用 | 1.6.0 | eigen_centrality(options = ‘必须是列表’) | c(“arpack_defaults() 现在是一个函数，请使用 options = arpack_defaults() 而不是 options = arpack_defaults。”) |
| 1 – 软弃用 | 2.1.1 | eigen_centrality(scale) | eigen_centrality() 的行为将始终如同使用了 scale=TRUE 一样。 |
| 2 – 警告 | 2.1.1 | eigen_centrality(scale = ‘始终如同 TRUE’) | 始终执行归一化 |
| 1 – 软弃用 | 2.1.0 | authority_score() | hits_scores() |
| 1 – 软弃用 | 1.6.0 | I(“arpack_defaults”) | arpack_defaults() |
| 1 – 软弃用 | 2.0.3 | hub_score() | hits_scores() |
| 1 – 软弃用 | 1.6.0 | I(“arpack_defaults”) | arpack_defaults() |
| 1 – 软弃用 | 2.0.0 | centralize.scores() | centralize() |
| 1 – 软弃用 | 2.0.0 | centralization.evcent.tmax() | centr_eigen_tmax() |
| 1 – 软弃用 | 2.0.0 | centralization.evcent() | centr_eigen() |
| 1 – 软弃用 | 2.0.0 | centralization.degree.tmax() | centr_degree_tmax() |
| 1 – 软弃用 | 2.0.0 | centralization.degree() | centr_degree() |
| 1 – 软弃用 | 2.0.0 | centralization.closeness.tmax() | centr_clo_tmax() |
| 1 – 软弃用 | 2.0.0 | centralization.closeness() | centr_clo() |
| 1 – 软弃用 | 2.0.0 | centralization.betweenness.tmax() | centr_betw_tmax() |
| 1 – 软弃用 | 2.0.0 | centralization.betweenness() | centr_betw() |
| 2 – 警告 | 2.0.0 | centr_degree_tmax(loops = ‘必须显式指定’) | 默认值（当前为 FALSE）将在下一个版本中删除。请为 loops 参数添加显式值。 |
| 1 – 软弃用 | 2.2.0 | centr_eigen(scale = ) | 该函数的行为始终如同 scale = TRUE。 |
| 该参数将在未来版本中删除。 | |||
| 1 – 软弃用 | 2.2.0 | centr_eigen_tmax(scale = ) | 该函数的行为始终如同 scale = TRUE。 |
| 该参数将在未来版本中删除。 | |||
| 1 – 软弃用 | 2.0.0 | maximal.independent.vertex.sets() | max_ivs() |
| 1 – 软弃用 | 2.0.0 | maximal.cliques.count() | count_max_cliques() |
| 1 – 软弃用 | 2.0.0 | maximal.cliques() | max_cliques() |
| 1 – 软弃用 | 2.0.0 | largest.independent.vertex.sets() | largest_ivs() |
| 1 – 软弃用 | 2.0.0 | largest.cliques() | largest_cliques() |
| 1 – 软弃用 | 2.0.0 | independent.vertex.sets() | ivs() |
| 1 – 软弃用 | 2.0.0 | independence.number() | ivs_size() |
| 1 – 软弃用 | 2.0.0 | clique.number() | clique_num() |
| 1 – 软弃用 | 2.1.0 | maximal_ivs() | max_ivs() |
| 1 – 软弃用 | 2.0.0 | exportPajek() | export_pajek() |
| 1 – 软弃用 | 2.0.0 | plotHierarchy() | plot_hierarchy() |
| 1 – 软弃用 | 2.0.0 | maxcohesion() | max_cohesion() |
| 1 – 软弃用 | 2.0.0 | graph.cohesion() | cohesion() |
| 1 – 软弃用 | 2.0.0 | cohesive.blocks() | cohesive_blocks() |
| 1 – 软弃用 | 2.0.0 | blockGraphs() | graphs_from_cohesive_blocks() |
| 1 – 软弃用 | 2.0.0 | create.communities() | make_clusters() |
| 1 – 软弃用 | 2.0.0 | walktrap.community() | cluster_walktrap() |
| 1 – 软弃用 | 2.0.0 | spinglass.community() | cluster_spinglass() |
| 1 – 软弃用 | 2.0.0 | showtrace() | show_trace() |
| 1 – 软弃用 | 2.0.0 | optimal.community() | cluster_optimal() |
| 1 – 软弃用 | 2.0.0 | multilevel.community() | cluster_louvain() |
| 1 – 软弃用 | 2.0.0 | mod.matrix() | modularity_matrix() |
| 1 – 软弃用 | 2.0.0 | leading.eigenvector.community() | cluster_leading_eigen() |
| 1 – 软弃用 | 2.0.0 | label.propagation.community() | cluster_label_prop() |
| 1 – 软弃用 | 2.0.0 | is.hierarchical() | is_hierarchical() |
| 1 – 软弃用 | 2.0.0 | infomap.community() | cluster_infomap() |
| 1 – 软弃用 | 2.0.0 | fastgreedy.community() | cluster_fast_greedy() |
| 1 – 软弃用 | 2.0.0 | edge.betweenness.community() | cluster_edge_betweenness() |
| 1 – 软弃用 | 2.0.0 | dendPlot() | plot_dendrogram() |
| 1 – 软弃用 | 2.0.0 | cutat() | cut_at() |
| 1 – 软弃用 | 2.0.0 | contract.vertices() | contract() |
| 1 – 软弃用 | 2.0.0 | code.length() | code_len() |
| 2 – 警告 | 2.1.0 | modularity_matrix(membership = ‘不再使用’) | |
| 1 – 软弃用 | 2.1.0 | cluster_leiden(resolution_parameter) | cluster_leiden(resolution) |
| 1 – 软弃用 | 1.6.0 | cluster_leading_eigen(options = ‘必须是列表’) | c(“arpack_defaults() 现在是一个函数，请使用 options = arpack_defaults() 而不是 options = arpack_defaults。”) |
| 1 – 软弃用 | 1.6.0 | cluster_label_prop(… = ) | 参数 initial 和 fixed 必须被命名。 |
| 1 – 软弃用 | 2.0.0 | no.clusters() | count_components() |
| 1 – 软弃用 | 2.0.0 | decompose.graph() | decompose() |
| 1 – 软弃用 | 2.0.0 | cluster.distribution() | component_distribution() |
| 1 – 软弃用 | 2.0.0 | biconnected.components() | biconnected_components() |
| 1 – 软弃用 | 2.0.0 | articulation.points() | articulation_points() |
| 1 – 软弃用 | 2.0.0 | igraph.console() | console() |
| 1 – 软弃用 | 2.0.0 | igraph.to.graphNEL() | as_graphnel() |
| 1 – 软弃用 | 2.0.0 | igraph.from.graphNEL() | graph_from_graphnel() |
| 1 – 软弃用 | 2.0.0 | graph.adjlist() | graph_from_adj_list() |
| 1 – 软弃用 | 2.0.0 | get.incidence() | as_biadjacency_matrix() |
| 1 – 软弃用 | 2.0.0 | get.edgelist() | as_edgelist() |
| 1 – 软弃用 | 2.0.0 | get.data.frame() | as_data_frame() |
| 1 – 软弃用 | 2.0.0 | get.adjacency() | as_adjacency_matrix() |
| 1 – 软弃用 | 2.0.0 | get.adjlist() | as_adj_list() |
| 1 – 软弃用 | 2.0.0 | get.adjedgelist() | as_adj_edge_list() |
| 1 – 软弃用 | 2.1.0 | get.adjacency.dense(loops = ‘必须是字符’) | sprintf(“转换为 get.adjacency.dense (loops = ‘%s’)”,loops) |
| 3 – 停止使用 | 2.0.0 | as_adjacency_matrix(edges = ) | |
| 1 – 软弃用 | 2.1.0 | as_adj() | as_adjacency_matrix() |
| 1 – 软弃用 | 1.6.0 | as_incidence_matrix() | as_biadjacency_matrix() |
| 1 – 软弃用 | 2.1.0 | as.directed() | as_directed() |
| 1 – 软弃用 | 2.1.0 | as.undirected() | as_undirected() |
| 1 – 软弃用 | 2.0.0 | graph.edgelist() | graph_from_edgelist() |
| 1 – 软弃用 | 2.0.0 | graph.data.frame() | graph_from_data_frame() |
| 1 – 软弃用 | 2.0.0 | is.chordal() | is_chordal() |
| 1 – 软弃用 | 2.0.0 | is.graphical.degree.sequence() | is_graphical() |
| 1 – 软弃用 | 2.0.0 | is.degree.sequence() | is_degseq() |
| 1 – 软弃用 | 2.0.0 | igraphdemo() | igraph_demo() |
| 1 – 软弃用 | 2.0.0 | power.law.fit() | fit_power_law() |
| 3 – 停止使用 | 2.1.0 | fit_power_law(implementation = “不能为 \”plfit.p\““) | I(‘fit_power_law(implementation = "plfit", p.value = TRUE)’) |
| 1 – 软弃用 | 2.0.0 | vertex.disjoint.paths() | vertex_disjoint_paths() |
| 1 – 软弃用 | 2.0.0 | vertex.connectivity() | vertex_connectivity() |
| 1 – 软弃用 | 2.0.0 | stMincuts() | st_min_cuts() |
| 1 – 软弃用 | 2.0.0 | stCuts() | st_cuts() |
| 1 – 软弃用 | 2.0.0 | minimum.size.separators() | min_separators() |
| 1 – 软弃用 | 2.0.0 | minimal.st.separators() | min_st_separators() |
| 1 – 软弃用 | 2.0.0 | is.separator() | is_separator() |
| 1 – 软弃用 | 2.0.0 | is.minimal.separator() | is_min_separator() |
| 1 – 软弃用 | 2.0.0 | graph.mincut() | min_cut() |
| 1 – 软弃用 | 2.0.0 | graph.maxflow() | max_flow() |
| 1 – 软弃用 | 2.0.0 | graph.adhesion() | adhesion() |
| 1 – 软弃用 | 2.0.0 | edge.disjoint.paths() | edge_connectivity() |
| 1 – 软弃用 | 2.0.0 | edge.connectivity() | edge_connectivity() |
| 1 – 软弃用 | 2.0.0 | dominator.tree() | dominator_tree() |
| 1 – 软弃用 | 2.0.0 | write.graph() | write_graph() |
| 1 – 软弃用 | 2.0.0 | read.graph() | read_graph() |
| 1 – 软弃用 | 2.0.0 | graph.graphdb() | graph_from_graphdb() |
| 1 – 软弃用 | 2.0.0 | watts.strogatz.game() | sample_smallworld() |
| 1 – 软弃用 | 2.0.0 | static.power.law.game() | sample_fitness_pl() |
| 1 – 软弃用 | 2.0.0 | static.fitness.game() | sample_fitness() |
| 1 – 软弃用 | 2.0.0 | sbm.game() | sample_sbm() |
| 1 – 软弃用 | 2.0.0 | preference.game() | sample_pref() |
| 1 – 软弃用 | 2.0.0 | lastcit.game() | sample_last_cit() |
| 1 – 软弃用 | 2.0.0 | k.regular.game() | sample_k_regular() |
| 1 – 软弃用 | 2.0.0 | interconnected.islands.game() | sample_islands() |
| 1 – 软弃用 | 2.0.0 | grg.game() | sample_grg() |
| 1 – 软弃用 | 2.0.0 | growing.random.game() | sample_growing() |
| 1 – 软弃用 | 2.0.0 | forest.fire.game() | sample_forestfire() |
| 1 – 软弃用 | 2.0.0 | establishment.game() | sample_traits() |
| 1 – 软弃用 | 2.0.0 | degree.sequence.game() | sample_degseq() |
| 1 – 软弃用 | 2.0.0 | connect.neighborhood() | connect() |
| 1 – 软弃用 | 2.0.0 | citing.cited.type.game() | sample_cit_cit_types() |
| 1 – 软弃用 | 2.0.0 | cited.type.game() | sample_cit_types() |
| 1 – 软弃用 | 2.0.0 | callaway.traits.game() | sample_traits_callaway() |
| 2 – 警告 | 2.0.0 | bipartite.random.game() | 使用 sample_bipartite_gnp() 或 sample_bipartite_gnm() |
| 1 – 软弃用 | 2.0.0 | barabasi.game() | sample_pa() |
| 1 – 软弃用 | 2.0.0 | ba.game() | sample_pa() |
| 1 – 软弃用 | 2.0.0 | asymmetric.preference.game() | sample_asym_pref() |
| 1 – 软弃用 | 2.0.0 | aging.barabasi.game() | sample_pa_age() |
| 1 – 软弃用 | 2.0.0 | aging.ba.game() | sample_pa_age() |
| 1 – 软弃用 | 2.0.0 | aging.prefatt.game() | sample_pa_age() |
| 1 – 软弃用 | 0.8.0 | erdos.renyi.game() | sample_gnp() |
| 1 – 软弃用 | 0.8.0 | erdos.renyi.game() | sample_gnm() |
| 1 – 软弃用 | 0.8.0 | random.graph.game() | sample_gnp() |
| 1 – 软弃用 | 0.8.0 | random.graph.game() | sample_gnm() |
| 2 – 警告 | 2.1.0 | sample_degseq(method = ‘必须是 configuration 而不是 simple’) | |
| 2 – 警告 | 2.1.0 | sample_degseq(method = ‘必须是 fast.heur.simple 而不是 simple.no.multiple’) | |
| 2 – 警告 | 2.1.0 | sample_degseq(method = ‘必须是 configuration.simple 而不是 simple.no.multiple.uniform’) | |
| 1 – 软弃用 | 2.2.0 | sample_bipartite() | sample_bipartite_gnp() |
| 1 – 软弃用 | 2.2.0 | sample_bipartite() | sample_bipartite_gnm() |
| 1 – 软弃用 | 2.1.3 | bipartite() | bipartite_gnp() |
| 1 – 软弃用 | 2.1.3 | bipartite() | bipartite_gnm() |
| 1 – 软弃用 | 2.0.0 | graphlets.project() | graphlet_proj() |
| 1 – 软弃用 | 2.0.0 | graphlets.candidate.basis() | graphlet_basis() |
| 1 – 软弃用 | 2.0.0 | hrg.predict() | predict_edges() |
| 1 – 软弃用 | 2.0.0 | hrg.fit() | fit_hrg() |
| 1 – 软弃用 | 2.0.0 | hrg.game() | sample_hrg() |
| 1 – 软弃用 | 2.0.0 | hrg.dendrogram() | hrg_tree() |
| 1 – 软弃用 | 2.0.0 | hrg.create() | hrg() |
| 1 – 软弃用 | 2.0.0 | hrg.consensus() | consensus_tree() |
| 1 – 软弃用 | 2.0.0 | graph.incidence() | graph_from_biadjacency_matrix() |
| 1 – 软弃用 | 1.6.0 | graph_from_incidence_matrix() | graph_from_biadjacency_matrix() |
| 1 – 软弃用 | 1.6.0 | graph_from_incidence_matrix() | graph_from_biadjacency_matrix() |
| 1 – 软弃用 | 2.0.0 | is.directed() | is_directed() |
| 1 – 软弃用 | 2.0.0 | delete.vertices() | delete_vertices() |
| 1 – 软弃用 | 2.0.0 | delete.edges() | delete_edges() |
| 1 – 软弃用 | 2.0.0 | add.vertices() | add_vertices() |
| 1 – 软弃用 | 2.0.0 | add.edges() | add_edges() |
| 3 – 停止使用 | 2.1.5 | get_edge_ids(vp = ‘不允许是 2 行 2 列的矩阵’) | |
| 2 – 警告 | 2.1.5 | get_edge_ids(vp = ‘作为矩阵提供的应该是 n 行 2 列的矩阵，而不是 2 行 n 列’) | 请使用 t() 转置矩阵，或者使用两列将其转换为 data.frame。 |
| 3 – 停止使用 | 2.0.0 | get.edge.ids(multi = ) | |
| 1 – 软弃用 | 2.0.0 | get.edge.ids(multi = ) | |
| 1 – 软弃用 | 2.1.0 | get.edge.ids() | get_edge_ids() |
| 3 – 停止使用 | 2.1.0 | nei() | .nei() |
| 3 – 停止使用 | 2.1.0 | innei() | .innei() |
| 3 – 停止使用 | 2.1.0 | outnei() | .outnei() |
| 3 – 停止使用 | 2.1.0 | inc() | .inc() |
| 3 – 停止使用 | 2.1.0 | adj() | .inc() |
| 3 – 停止使用 | 2.1.0 | from() | .from() |
| 3 – 停止使用 | 2.1.0 | to() | .to() |
| 3 – 停止使用 | 2.1.0 | adj() | .inc() |
| 3 – 停止使用 | 2.1.0 | inc() | .inc() |
| 3 – 停止使用 | 2.1.0 | from() | .from() |
| 3 – 停止使用 | 2.1.0 | to() | .to() |
| 1 – 软弃用 | 2.0.0 | piecewise.layout() | layout_components() |
| 1 – 软弃用 | 2.0.0 | layout.sugiyama() | layout_with_sugiyama() |
| 1 – 软弃用 | 2.0.0 | layout.star() | layout_as_star() |
| 1 – 软弃用 | 2.0.0 | layout.norm() | norm_coords() |
| 1 – 软弃用 | 2.0.0 | layout.merge() | merge_coords() |
| 1 – 软弃用 | 2.0.0 | layout.mds() | layout_with_mds() |
| 1 – 软弃用 | 2.0.0 | layout.grid() | layout_on_grid() |
| 1 – 软弃用 | 2.0.0 | layout.graphopt() | layout_with_graphopt() |
| 1 – 软弃用 | 2.0.0 | layout.gem() | layout_with_gem() |
| 1 – 软弃用 | 2.0.0 | layout.davidson.harel() | layout_with_dh() |
| 1 – 软弃用 | 2.0.0 | layout.bipartite() | layout_as_bipartite() |
| 1 – 软弃用 | 2.0.0 | layout.auto() | layout_nicely() |
| 1 – 软弃用 | 2.1.0 | layout.reingold.tilford() | layout_as_tree() |
| 1 – 软弃用 | 2.1.0 | layout.circle() | layout_in_circle() |
| 3 – 停止使用 | 2.1.0 | layout.grid.3d() | layout_on_grid() |
| 1 – 软弃用 | 2.1.0 | layout.sphere() | layout_on_sphere() |
| 1 – 软弃用 | 2.1.0 | layout.random() | layout_randomly() |
| 3 – 停止使用 | 0.8.0 | layout_with_fr(coolexp = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_fr(maxdelta = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_fr(area = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_fr(repulserad = ) | |
| 1 – 软弃用 | 2.1.0 | layout.fruchterman.reingold() | layout_with_fr() |
| 3 – 停止使用 | 0.8.0 | layout_with_kk(niter = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_kk(sigma = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_kk(initemp = ) | |
| 3 – 停止使用 | 0.8.0 | layout_with_kk(coolexp = ) | |
| 1 – 软弃用 | 2.1.0 | layout.kamada.kawai() | layout_with_kk() |
| 1 – 软弃用 | 2.1.0 | layout.lgl() | layout_with_lgl() |
| 1 – 软弃用 | 1.6.0 | layout_with_mds(options = ‘必须是列表’) | c(“arpack_defaults() 现在是一个函数，请使用 options = arpack_defaults() 而不是 options = arpack_defaults。”) |
| 2 – 警告 | 2.1.0 | layout.spring() | layout_with_fr() |
| 2 – 警告 | 2.1.0 | layout.svd() | layout_with_fr() |
| 2 – 警告 | 2.1.0 | layout.fruchterman.reingold.grid() | layout_with_fr() |
| 1 – 软弃用 | 2.0.0 | layout.drl() | layout_with_drl() |
| 1 – 软弃用 | 2.1.0 | graph() | make_graph() |
| 1 – 软弃用 | 2.1.0 | graph.famous() | make_graph() |
| 1 – 软弃用 | 2.1.0 | line.graph() | make_line_graph() |
| 1 – 软弃用 | 2.1.0 | graph.ring() | make_ring() |
| 1 – 软弃用 | 2.1.0 | graph.tree() | make_tree() |
| 1 – 软弃用 | 2.1.0 | graph.star() | make_star() |
| 1 – 软弃用 | 2.1.0 | graph.lcf() | graph_from_lcf() |
| 1 – 软弃用 | 2.1.0 | graph.lattice() | make_lattice() |
| 1 – 软弃用 | 2.0.3 | graph.lattice(circular = ‘使用 periodic 参数代替’) | c(“circular 现在已弃用，请改用 periodic。”) |
| 1 – 软弃用 | 2.1.0 | graph.kautz() | make_kautz_graph() |
| 1 – 软弃用 | 2.1.0 | graph.full.citation() | make_full_citation_graph() |
| 1 – 软弃用 | 2.1.0 | graph.full.bipartite() | make_full_bipartite_graph() |
| 1 – 软弃用 | 2.1.0 | graph.full() | make_full_graph() |
| 1 – 软弃用 | 2.1.0 | graph.formula() | graph_from_literal() |
| 1 – 软弃用 | 2.1.0 | graph.extended.chordal.ring() | make_chordal_ring() |
| 1 – 软弃用 | 2.1.0 | graph.empty() | make_empty_graph() |
| 1 – 软弃用 | 2.1.0 | graph.de.bruijn() | make_de_bruijn_graph() |
| 1 – 软弃用 | 2.1.0 | graph.bipartite() | make_bipartite_graph() |
| 1 – 软弃用 | 2.1.0 | graph.atlas() | graph_from_atlas() |
| 1 – 软弃用 | 2.1.0 | graph_() | c(“请直接使用构造函数，例如 graph_from_edgelist()。”,“graph_() 将在 igraph 的未来版本中删除。”) |
| 1 – 软弃用 | 2.0.3 | make_lattice(circular = ‘使用 periodic 参数代替’) | c(“circular 现在已弃用，请改用 periodic。”) |
| 1 – 软弃用 | 2.0.0 | minimum.spanning.tree() | mst() |
| 1 – 软弃用 | 2.0.0 | triad.census() | triad_census() |
| 1 – 软弃用 | 2.0.0 | graph.motifs.no() | count_motifs() |
| 1 – 软弃用 | 2.0.0 | graph.motifs.est() | sample_motifs() |
| 1 – 软弃用 | 2.0.0 | graph.motifs() | motifs() |
| 1 – 软弃用 | 2.0.0 | dyad.census() | dyad_census() |
| 1 – 软弃用 | 2.0.0 | graph.intersection() | intersection() |
| 1 – 软弃用 | 2.0.0 | graph.union() | union.igraph() |
| 1 – 软弃用 | 2.0.0 | graph.difference() | difference() |
| 1 – 软弃用 | 2.0.0 | graph.disjoint.union() | disjoint_union() |
| 1 – 软弃用 | 2.0.0 | graph.compose() | compose() |
| 1 – 软弃用 | 2.0.0 | graph.complementer() | complementer() |
| 1 – 软弃用 | 2.0.0 | running.mean() | running_mean() |
| 1 – 软弃用 | 2.0.0 | igraph.sample() | sample_seq() |
| 1 – 软弃用 | 2.0.0 | convex.hull() | convex_hull() |
| 1 – 软弃用 | 2.0.0 | igraph.options() | igraph_options() |
| 1 – 软弃用 | 2.0.0 | getIgraphOpt() | igraph_opt() |
| 1 – 软弃用 | 2.0.0 | path.length.hist() | distance_table() |
| 1 – 软弃用 | 2.0.0 | maximum.cardinality.search() | max_cardinality() |
| 1 – 软弃用 | 2.0.0 | is.dag() | is_dag() |
| 1 – 软弃用 | 2.1.0 | eccentricity(… =) | 参数 mode 必须被命名。 |
| 1 – 软弃用 | 2.1.0 | radius(… =) | 参数 mode 必须被命名。 |
| 1 – 软弃用 | 2.0.0 | autocurve.edges() | curve_multiple() |
| 1 – 软弃用 | 2.0.0 | igraph.shape.noplot() | shape_noplot() |
| 1 – 软弃用 | 2.0.0 | igraph.shape.noclip() | shape_noclip() |
| 1 – 软弃用 | 2.0.0 | vertex.shapes() | shapes() |
| 1 – 软弃用 | 2.0.0 | add.vertex.shape() | add_shape() |
| 1 – 软弃用 | 2.1.0 | similarity.jaccard() | similarity(method) |
| 1 – 软弃用 | 2.1.0 | similarity.dice() | similarity(method) |
| 1 – 软弃用 | 2.1.0 | similarity.invlogweighted() | similarity(method) |
| 1 – 软弃用 | 2.0.0 | is.simple() | is_simple() |
| 1 – 软弃用 | 2.0.0 | get.stochastic() | stochastic_matrix() |
| 1 – 软弃用 | 2.0.0 | get.shortest.paths() | shortest_paths() |
| 1 – 软弃用 | 2.0.0 | get.all.shortest.paths() | all_shortest_paths() |
| 1 – 软弃用 | 2.0.0 | get.diameter() | get_diameter() |
| 1 – 软弃用 | 2.0.0 | unfold.tree() | unfold_tree() |
| 1 – 软弃用 | 2.0.0 | topological.sort() | topo_sort() |
| 1 – 软弃用 | 2.0.0 | shortest.paths() | distances() |
| 1 – 软弃用 | 2.0.0 | neighborhood.size() | ego_size() |
| 1 – 软弃用 | 2.0.0 | maximum.bipartite.matching() | max_bipartite_match() |
| 1 – 软弃用 | 2.0.0 | is.mutual() | which_mutual() |
| 1 – 软弃用 | 2.0.0 | is.multiple() | which_multiple() |
| 1 – 软弃用 | 2.0.0 | is.maximal.matching() | is_max_matching() |
| 1 – 软弃用 | 2.0.0 | is.matching() | is_matching() |
| 1 – 软弃用 | 2.0.0 | is.loop() | which_loop() |
| 1 – 软弃用 | 2.0.0 | is.connected() | is_connected() |
| 1 – 软弃用 | 2.0.0 | induced.subgraph() | induced_subgraph() |
| 1 – 软弃用 | 2.0.0 | has.multiple() | any_multiple() |
| 1 – 软弃用 | 2.0.0 | graph.neighborhood() | make_ego_graph() |
| 1 – 软弃用 | 2.0.0 | graph.laplacian() | laplacian_matrix() |
| 1 – 软弃用 | 2.0.0 | graph.knn() | knn() |
| 1 – 软弃用 | 2.0.0 | graph.dfs() | dfs() |
| 1 – 软弃用 | 2.0.0 | graph.density() | edge_density() |
| 1 – 软弃用 | 2.0.0 | graph.coreness() | coreness() |
| 1 – 软弃用 | 2.0.0 | graph.bfs() | bfs() |
| 1 – 软弃用 | 2.0.0 | farthest.nodes() | farthest_vertices() |
| 1 – 软弃用 | 2.0.0 | degree.distribution() | degree_distribution() |
| 1 – 软弃用 | 2.0.0 | count.multiple() | count_multiple() |
| 1 – 软弃用 | 2.0.0 | clusters() | components() |
| 1 – 软弃用 | 2.0.0 | average.path.length() | mean_distance() |
| 1 – 软弃用 | 2.1.0 | subgraph.edges() | subgraph_from_edges() |
| 3 – 停止使用 | 1.3.0 | bfs(neimode = ) | bfs(mode = ) |
| 2 – 警告 | 2.2.0 | bfs(father = ) | bfs(parent = ) |
| 3 – 停止使用 | 1.3.0 | dfs(neimode = ) | dfs(mode = ) |
| 2 – 警告 | 2.2.0 | dfs(father = ) | dfs(parent = ) |
| 1 – 软弃用 | 2.0.3 | make_lattice(normalized = ‘提供 normalization 代替’) | c(“normalized 现在已弃用，请改用 normalization。”) |
| 1 – 软弃用 | 2.0.0 | are.connected() | are_adjacent() |
| 1 – 软弃用 | 2.0.0 | tkplot.setcoords() | tk_set_coords() |
| 1 – 软弃用 | 2.0.0 | tkplot.rotate() | tk_rotate() |
| 1 – 软弃用 | 2.0.0 | tkplot.reshape() | tk_reshape() |
| 1 – 软弃用 | 2.0.0 | tkplot.off() | tk_off() |
| 1 – 软弃用 | 2.0.0 | tkplot.getcoords() | tk_coords() |
| 1 – 软弃用 | 2.0.0 | tkplot.fit.to.screen() | tk_fit() |
| 1 – 软弃用 | 2.0.0 | tkplot.export.postscript() | tk_postscript() |
| 1 – 软弃用 | 2.0.0 | tkplot.close() | tk_close() |
| 1 – 软弃用 | 2.0.0 | tkplot.center() | tk_center() |
| 1 – 软弃用 | 2.0.0 | tkplot.canvas() | tk_canvas() |
| 1 – 软弃用 | 2.0.0 | permute.vertices() | permute() |
| 1 – 软弃用 | 2.0.0 | graph.isocreate() | graph_from_isomorphism_class() |
| 1 – 软弃用 | 2.0.0 | graph.automorphisms() | count_automorphisms() |
| 1 – 软弃用 | 2.0.0 | canonical.permutation() | canonical_permutation() |
| 1 – 软弃用 | 2.0.0 | automorphisms() | count_automorphisms() |
| 1 – 软弃用 | 2.0.0 | adjacent.triangles() | count_triangles() |
| 1 – 软弃用 | 2.0.0 | igraph.version() | igraph_version() |