In　networked　data,　linked　objects　tend　to　belong　to　the　same　class,　and　densely　linked　sub-graphs　are　often　available.　Based　on　these　facts,　this　paper　presents　a　regularization　framework　that　consists　of　fitting　and　regularization　terms　for　transductive　learning　in　networked　data.　The　desirable　value　of　the　fitting　term　is　related　to　the　number　of　labeled　data,　whereas　that　of　the　regularization　term　is　dependent　on　the　structure　of　the　graph.　The　ratio　of　these　two　desirable　values　is　essential　for　the　estimation　of　the　optimal　regularization　parameters,　such　as　that　proposed　in　our　paper.　Under　the　proposed　regularization　framework,　an　effective　classification　algorithm　is　developed.　Two　methods　are　also　introduced　to　incorporate　contents　of　objects　into　the　proposed　framework　to　ultimately　improve　classification　accuracy.　Promising　experimental　results　are　reported　on　a　toy　problem　and　a　paper　classification　task.　(C)　2012　Elsevier　Inc.　All　rights　reserved.