/home/u548518200/domains/pdbi.ac.id/public_html/library/lib/SearchEngine/DefaultEngine.php:615 "Search Engine Debug 🔎 🪲"
Engine Type ⚙️: "SLiMS\SearchEngine\DefaultEngine"
SQL ⚙️: array:2 [ "count" => "select count(distinct b.biblio_id) from biblio as b left join mst_publisher as mp on b.publisher_id=mp.publisher_id left join mst_place as mpl on b.publish_place_id=mpl.place_id where b.opac_hide=0 and (b.biblio_id in(select ba.biblio_id from biblio_author as ba left join mst_author as ma on ba.author_id=ma.author_id where ma.author_name like ?))" "query" => "select b.biblio_id, b.title, b.image, b.isbn_issn, b.publish_year, mp.publisher_name as `publisher`, mpl.place_name as `publish_place`, b.labels, b.input_date, b.edition, b.collation, b.series_title, b.call_number from biblio as b left join mst_publisher as mp on b.publisher_id=mp.publisher_id left join mst_place as mpl on b.publish_place_id=mpl.place_id where b.opac_hide=0 and (b.biblio_id in(select ba.biblio_id from biblio_author as ba left join mst_author as ma on ba.author_id=ma.author_id where ma.author_name like ?)) order by b.last_update desc limit 10 offset 0" ]
Bind Value ⚒️: array:1 [ 0 => "%Riccardo Rastell%" ]
The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualizeā¦