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{"title"=>"Detecting memory and structure in human navigation patterns using Markov chain models of varying order", "type"=>"journal", "authors"=>[{"first_name"=>"Philipp", "last_name"=>"Singer", "scopus_author_id"=>"55317172300"}, {"first_name"=>"Denis", "last_name"=>"Helic", "scopus_author_id"=>"6506005845"}, {"first_name"=>"Behnam", "last_name"=>"Taraghi", "scopus_author_id"=>"42862622600"}, {"first_name"=>"Markus", "last_name"=>"Strohmaier", "scopus_author_id"=>"17347360600"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"373524559", "arxiv"=>"arXiv:1402.0790v2", "pmid"=>"25013937", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0102070", "scopus"=>"2-s2.0-84904252309", "sgr"=>"84904252309"}, "id"=>"796cb465-33d2-3c4e-989e-6aab2f73ee50", "abstract"=>"One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.", "link"=>"http://www.mendeley.com/research/detecting-memory-structure-human-navigation-patterns-using-markov-chain-models-varying-order", "reader_count"=>29, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>3, "Student > Doctoral Student"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>10, "Student > Master"=>7, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>3, "Student > Doctoral Student"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>10, "Student > Master"=>7, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>2, "Mathematics"=>4, "Agricultural and Biological Sciences"=>3, "Business, Management and Accounting"=>2, "Physics and Astronomy"=>2, "Chemistry"=>1, "Computer Science"=>11, "Immunology and Microbiology"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>3}, "Computer Science"=>{"Computer Science"=>11}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "United States"=>1, "India"=>1}, "group_count"=>0}

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• http://doi.org/10.1080/13614568.2016.1179798
• http://doi.org/10.1007/s10660-016-9226-7
• http://doi.org/10.1063/1.5024814
• http://doi.org/10.1061/JTEPBS.0000237
• http://doi.org/10.2196/11934
• http://doi.org/10.1038/srep41951
• http://doi.org/10.1088/1367-2630/ab13fb
• http://doi.org/10.3389/fncom.2017.00080
• http://doi.org/10.1016/j.neuron.2018.07.047
• http://doi.org/10.1126/sciadv.1600028
• http://doi.org/10.3758/s13428-015-0604-1
• http://doi.org/10.1371/journal.pone.0170746
• http://doi.org/10.1016/j.ijhcs.2015.07.006
• http://doi.org/10.1007/s41109-017-0036-1
• http://doi.org/10.1098/rsos.182174
• http://doi.org/10.1016/j.ejor.2017.06.006
• http://doi.org/10.1007/s10618-017-0518-x
• http://doi.org/10.1093/comnet/cnx031
• http://doi.org/10.1098/rsos.160460
• http://doi.org/10.1016/j.jlamp.2018.07.003

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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/e4ce7275-715c-410f-93b5-724100eb1157", "title"=>"Equation 20 production mistake", "body"=>"In Equation 20 the first denominator should consist alpha~~ij~~instead of x~~ij~~. This was a mistake in production.", "isRemoved"=>false, "created"=>"2014-09-12T17:25:17Z", "lastModified"=>"2014-09-12T17:25:17Z", "creator"=>{"userId"=>"442991"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0102070", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2014-09-12T17:25:17Z", "replies"=>[]}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1589675"], "description"=>"<p>The top row shows results obtained using likelihood and information theoretic results: (A) likelihoods, (B) likelihood ratio statistics (* statistically significant at the 1% level; ** statistically significant at the 0.1% level) as well as AIC (C) and BIC (D) statistics. The bottom row illustrates results obtained from Bayesian Inference: (E) shows evidence and (F) Bayesian model selection. (G) presents the results from cross validation. The overall results suggest that higher order chains seem to be more appropriate for our navigation paths consisting of topics. Specifically, the results suggest a third order Markov chain model.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "msnbc"], "article_id"=>1100992, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g007", "stats"=>{"downloads"=>4, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_MSNBC_dataset_/1100992", "title"=>"Model selection results for the MSNBC dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589690"], "description"=>"<p>The number of times users stay within the same topic vs. the number of times they change the topic during navigation for different values of . Only the top three categories with the highest transition probabilities are shown. With high consistency, the transition probabilities to the same topic increase while those to other categories decrease with ascending order .</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "navigation", "msnbc"], "article_id"=>1101005, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g012", "stats"=>{"downloads"=>3, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Self_transition_structure_of_navigation_for_the_MSNBC_dataset_/1101005", "title"=>"Self transition structure of navigation for the MSNBC dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589673"], "description"=>"<p>The top row shows results obtained using likelihood and information theoretic results: (A) likelihoods, (B) likelihood ratio statistics (* statistically significant at the 1% level; ** statistically significant at the 0.1% level) as well as AIC (C) and BIC (D) statistics. The bottom row illustrates results obtained from Bayesian Inference: (E) shows evidence and (F) Bayesian model selection. (G) presents the results from cross validation. The overall results suggest that higher order chains seem to be more appropriate for our navigation paths consisting of topics. Concretely, we find that a second order Markov chain model for our Wikispeedia topic dataset best explains the data.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "wikispeedia"], "article_id"=>1100990, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g006", "stats"=>{"downloads"=>1, "page_views"=>49, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_Wikispeedia_dataset_/1100990", "title"=>"Model selection results for the Wikispeedia dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589670"], "description"=>"<p>The top row shows results obtained using likelihood and information theoretic results: (A) likelihoods, (B) likelihood ratio statistics (* statistically significant at the 1% level; ** statistically significant at the 0.1% level) as well as AIC (C) and BIC (D) statistics. The bottom row illustrates results obtained from Bayesian Inference: (E) evidence and (F) Bayesian model selection. Finally, the figure presents the results from (G) cross validation. The overall results suggest a zero order Markov chain model.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "wikigame"], "article_id"=>1100987, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g004", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_Wikigame_page_dataset_/1100987", "title"=>"Model selection results for the Wikigame page dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589686"], "description"=>"<p>The number of times users stay within the same topic vs. the number of times they change the topic during navigation for different orders for our Wikigame dataset. Only the top three categories with the highest transition probabilities are shown. With high consistency, the transition probabilities to the same topic increase while those to other categories decrease with ascending order .</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "navigation", "wikigame"], "article_id"=>1101000, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g010", "stats"=>{"downloads"=>5, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Self_transition_structure_of_navigation_for_the_Wikigame_topic_dataset_/1101000", "title"=>"Self transition structure of navigation for the Wikigame topic dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589681"], "description"=>"<p>The graphs above illustrate selected state transitions from the Wikigame topic dataset for different values. The nodes represent categories and the links illustrate transitions between categories. The link weight corresponds to the transition probability from the source to the target node determined by MLE. The node size corresponds to the sum of the incoming transition probabilities from all other nodes to that source node. In the left figure the top four categories with the highest incoming transition probabilities are illustrated for an order of . For those nodes we draw the four highest outgoing transition probabilities to other nodes. In the middle figure we visualize the Markov chain of order by setting the top topic (<i>Culture</i>) as the first click; this diagram shows transition probabilities from top four categories given that users first visited the <i>Culture</i> topic. For example, the links from the red node (<i>Society</i>) in the bottom-right part of the diagram represent the transition probabilities from the sequence (<i>Culture</i>, <i>Society</i>). Similarly, we visualize order in the right figure by selecting a node with the highest incoming probability (<i>Culture</i>, <i>Culture</i>) of order . We then show transition probabilities from other nodes given that users already visited (<i>Culture</i>, <i>Culture</i>). For example, the links from the brown node (<i>Politics</i>) at the top represent the transition probabilities from the sequence (<i>Culture</i>, <i>Culture</i>, <i>Politics</i>).</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "navigation", "wikigame"], "article_id"=>1100998, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g009", "stats"=>{"downloads"=>3, "page_views"=>39, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_structure_of_navigation_for_the_Wikigame_topic_dataset_/1100998", "title"=>"Local structure of navigation for the Wikigame topic dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589663"], "description"=>"<p>Simple log-likelihoods of varying Markov chain orders would suggest higher orders as the higher the order the higher the corresponding log-likelihoods are. This suggests that looking at these log-likelihoods is not enough for finding the appropriate Markov chain order as methods are necessary that balance the goodness-of-fit against the number of model parameters.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories"], "article_id"=>1100980, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Log_likelihoods_for_random_path_dataset_/1100980", "title"=>"Log-likelihoods for random path dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589692"], "description"=>"<p>The results should be compare with <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102070#pone-0102070-g008\" target=\"_blank\">Figure 8</a>. The results are split by only looking at a corpus of paths where each path starts with the same topic as it ends (A) and by looking at a corpus with distinct start and target categories (B).</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "patterns", "navigational", "wikigame"], "article_id"=>1101007, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g013", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Common_global_transition_patterns_of_navigational_behavior_on_the_Wikigame_topic_dataset_/1101007", "title"=>"Common global transition patterns of navigational behavior on the Wikigame topic dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589693"], "description"=>"<p>Dataset statistics.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories"], "article_id"=>1101008, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dataset_statistics_/1101008", "title"=>"Dataset statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589671"], "description"=>"<p>The top row shows results obtained using likelihood and information theoretic results: (A) likelihoods, (B) likelihood ratio statistics (* statistically significant at the 1% level; ** statistically significant at the 0.1% level) as well as AIC (C) and BIC (D) statistics. The bottom row illustrates results obtained from Bayesian Inference: (E) shows evidence and (F) Bayesian model selection. (G) presents the results from cross validation. The overall results suggest that higher order chains seem to be more appropriate for our navigation paths consisting of topics. In detail, we find that a second order Markov chain model for our Wikigame topic dataset best explains the data.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "wikigame"], "article_id"=>1100988, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g005", "stats"=>{"downloads"=>4, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_selection_results_for_the_Wikigame_topic_dataset_/1100988", "title"=>"Model selection results for the Wikigame topic dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589687"], "description"=>"<p>The graphs above illustrate selected state transitions from the MSNBC dataset for different values. The nodes represent categories and the links illustrate transitions between categories. The link weight corresponds to the transition probability from the source to the target node determined by MLE. The node size represents the global importance of a node in the whole dataset and corresponds to the sum of the outgoing transition probabilities from that node to all other nodes. For visualization reasons we primarily focus on the top four categories with the highest sum of outgoing transition probabilities – i.e., those with the largest node sizes – for an order of . For those nodes we draw the four highest outgoing transition probabilities to other nodes. In the middle figure we visualize the Markov chain of order by setting the top topic (frontpage) from order as the first click; this diagram shows transition probabilities from top four categories given that users first visited the frontpage topic (represented by the dashed transitions in the left figure representing ). For example, the links from the blue node (news) in the top-left corner of the diagram represent the transition probabilities from the sequence (frontpage, news) to other nodes. Similarly, we visualize order in the right figure by selecting a node with the highest sum of outgoing transition probabilities (frontpage, frontpage) and its four highest outgoing transition probabilities from order (represented by the dashed transitions in the middle figure representing ). We then show transition probabilities from other nodes given that users already visited (frontpage, frontpage). For example, the links from the red node (sports) at the top represent the transition probabilities from the sequence (frontpage, frontpage, sports) to other nodes.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "navigation", "msnbc"], "article_id"=>1101002, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g011", "stats"=>{"downloads"=>3, "page_views"=>74, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_structure_of_navigation_for_the_MSNBC_dataset_/1101002", "title"=>"Local structure of navigation for the MSNBC dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589667"], "description"=>"<p>Frequency of categories (in percent) of all paths in (A) the Wikigame topic dataset (B) the Wikispeedia dataset and (C) the MSNBC dataset. The colors indicate the categories we will investigate in detail later and are representative for a single dataset – this means that the same color in the datasets does not represent the same topic. The Wikigame topic dataset consists of more distinct categories than the Wikispeedia and MSNBC dataset. Furthermore, the most frequently occuring topic in the Wikigame topic dataset is Culture with around 13%. The Wikispeedia dataset is dominated by the two categories the most Science and Geography each making up for almost 25% of all clicks. Finally, the most frequent topic in the MSNBC dataset is the frontpage with a frequency of around 22%.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories"], "article_id"=>1100983, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g003", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topic_frequencies_/1100983", "title"=>"Topic frequencies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589680"], "description"=>"<p>Common transition patterns of navigational behavior on all three topics datasets (Wikigame, Wikispeedia and MSNBC). Patterns are illustrated by heatmaps calculated on the first order transition matrices. Each cell is normalized by the total number of transitions in the dataset. The vertical lines depict starting states and the horicontal lines depict target states. A main observation is that self transitions – e.g., a transition from <i>Culture</i> to <i>Culture</i> – are dominating all datasets. However, the goal-oriented datasets (Wikigame and Wikispeedia) exhibit more transitions between distinct categories than the free navigation dataset (MSNBC).</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories"], "article_id"=>1100997, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g008", "stats"=>{"downloads"=>5, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_structure_of_human_navigation_/1100997", "title"=>"Global structure of human navigation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}
• {"files"=>["https://ndownloader.figshare.com/files/1589661"], "description"=>"<p>Bottom row of nodes: A user navigates a series of Wikipedia articles, which can be represented as a sequence of Web pages. Top row of nodes: Each Wikipedia article can be mapped to a corresponding topic through Wikipedia's system of categories. This results in a sequence of topics.</p>", "links"=>[], "tags"=>["Computer modeling", "Computing methods", "mathematics", "Probability theory", "Bayes theorem", "Markov models", "Probability distribution", "Random variables", "Stochastic processes", "Statistics (mathematics)", "Statistical methods", "Statistical theories", "navigation", "wikigame"], "article_id"=>1100978, "categories"=>["Biological Sciences"], "users"=>["Philipp Singer", "Denis Helic", "Behnam Taraghi", "Markus Strohmaier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0102070.g001", "stats"=>{"downloads"=>5, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_a_navigation_sequence_in_the_WikiGame_dataset_/1100978", "title"=>"Example of a navigation sequence in the WikiGame dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-11 03:18:49"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime