Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: An optimized friend recommendation system is described. An active social network (ASN) of a player of an online game is determined based on a frequency of reciprocated communications between the player and friends of the player. The ASN of the player has friends of the player with a minimum number of closed-loop communications with the player within a predetermined duration. The social network of non-ASN friends outside the ASN of the player is identified. One or more non-ASN friends identified and suggested to socially engage with the player in an online game based on the social network of non-ASN friends of the player outside the ASN of the player are.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A method and a system for identifying players likely to spend in an online game are described. Indicators corresponding to activities of the player and in-game friends of the player in an online game and in other online games are determined. A prediction model of the player's likelihood to spend in the online game is computed based on the indicators. The prediction model is used to identify players of the online game that are likely to spend in the online game.

Abstract: An optimized friend recommendation system is described. An active social network (ASN) of a player of an online game is determined based on a frequency of reciprocated communications between the player and friends of the player. The ASN of the player has friends of the player with a minimum number of closed-loop communications with the player within a predetermined duration. The social network of non-ASN friends outside the ASN of the player is identified. One or more non-ASN friends identified and suggested to socially engage with the player in an online game based on the social network of non-ASN friends of the player outside the ASN of the player are.

Abstract: An optimized friend recommendation system is described. An active social network (ASN) of a player of an online game is determined based on a frequency of reciprocated communications between the player and friends of the player. The ASN of the player has friends of the player with a minimum number of closed-loop communications with the player within a predetermined duration. The social network of non-ASN friends outside the ASN of the player is identified. One or more non-ASN friends identified and suggested to socially engage with the player in an online game based on the social network of non-ASN friends of the player outside the ASN of the player are.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: An optimized friend recommendation system is described. An active social network (ASN) of a player of an online game is determined based on a frequency of reciprocated communications between the player and friends of the player. The ASN of the player has friends of the player with a minimum number of closed-loop communications with the player within a predetermined duration. The social network of non-ASN friends outside the ASN of the player is identified. One or more non-ASN friends identified and suggested to socially engage with the player in an online game based on the social network of non-ASN friends of the player outside the ASN of the player are.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: An optimized friend recommendation system is described. An active social network (ASN) of a player of an online game is determined based on a frequency of reciprocated communications between the player and friends of the player. The ASN of the player has friends of the player with a minimum number of closed-loop communications with the player within a predetermined duration. The social network of non-ASN friends outside the ASN of the player is identified. One or more non-ASN friends identified and suggested to socially engage with the player in an online game based on the social network of non-ASN friends of the player outside the ASN of the player are.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: Social network information of a player of a computer-implemented game is accessed to analyze a social interaction of the player with other players from the social network information to recommend other players of the social network of the player to further communicate with. A recommendation of other players is generated to the player based on a frequency of the social interaction. A selection of players from the recommendation is received from the player. A communication message is generated to the selected players.

Abstract: Social network information of a player of a computer-implemented game is accessed to identify active players from the social network information. The active players each have a minimum number of reciprocated communications with the player within a time range. A recommendation of players is generated to the player based on the identified active players.

Abstract: Social network information of a player of a computer-implemented game is accessed to identify non-active players from the social network information. The non-active players each have a minimum number of unreciprocated communications with the player within a time duration. A recommendation of the recommended players is generated to the player based on the non-active players.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A poultry wing receiving wheel is formed in three separable wheel segments, each of which have peripheral teeth which are aligned when such three segments are assembled so as to collectively form a plurality of circumferentially spaced poultry wing receiving troughs. By separably assembling the three wheel segments, fabrication is simplified and replacement costs are greatly reduced by permitting replacement of only the wheel segment which has worn or damaged peripheral teeth, i.e., the assembled wheel is to a degree rendered repairable. The assembled wheel may be uaed with a conventional apparatus for cutting apart poultry wings. The central most of the three wheel segments may be provided with an integral keyway for mounting on a drive shaft of such a conventional poultry wing cutting machine, with the two remaining segments axially aligned and bolted to such middle wheel segment on each axial side thereof.