C. Lee Giles – författare

Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

Thisyear’svolumeofAdvancesinWebMiningandWebUsageAnalysiscontains thepostworkshopproceedingsofajointevent,the9thInternationalWorkshopon Knowledge Discovery from the Web (WEBKDD 2007) and the First SNA-KDD Workshop on Social Network Analysis (SNA-KDD 2007). The joint workshop on Web Mining and Social Network Analysis took place at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). It attracted 23 submissions, of which 14 were accepted for presentation at the workshop. Eight of them have been extended for inclusion in this volume. WEBKDD is one of the most traditional workshops of the ACM SIGKDD internationalconference, under the auspices of which it has been organizedsince 1999. The strong interest for knowledge discovery in the Web, fostered not least by WEBKDD itself, has led to solutions for many problems in the Web’s p- mature era. In the meanwhile, the Web has stepped into a new era, where it is experienced as a social medium, fostering interaction among people, enabling and promoting the sharing of knowledge, experiences and applications, char- terized by group activities, community formation, and evolution. The design of Web 2. 0 re?ects the socialcharacterof the Web, bringing new potential and new challenges. The 9th WEBKDD was devoted to the challenges and opportunities of mining for the social Web and promptly gave rise to the joint event with the First Workshop on Social Network Analysis (SNA-KDD). Social network research has advanced signi?cantly in the last few years, strongly motivated by the prevalence of online social websites and a variety of large-scale o?ine social network systems.

This year’s volume of Advances in Social Network Analysis contains the p- ceedings for the Second International Workshop on Social Network Analysis (SNAKDD 2008). The annual workshop co-locates with the ACM SIGKDD - ternational Conference on Knowledge Discovery and Data Mining (KDD). The second SNAKDD workshop was held with KDD 2008 and received more than 32 submissions on social network mining and analysis topics. We accepted 11 regular papers and 8 short papers. Seven of the papers are included in this volume. In recent years, social network research has advanced signi?cantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale o?ine social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are - creasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topol- ical properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining socialnetworks.These issues haveimportant implications oncom- nitydiscovery,anomalydetection,trendpredictionandcanenhanceapplications in multiple domains such as information retrieval, recommendation systems, - curity and so on.