Results 1 - 10
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43
Modeling relationship strength in online social networks.
- In Proc. WWW
, 2010
"... ABSTRACT Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, t ..."
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Cited by 109 (4 self)
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ABSTRACT Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.
Suggesting Friends Using the Implicit Social Graph
"... Although users of online communication tools rarely categorize their contacts into groups such as ”family”, ”co-workers”, or ”jogging buddies”, they nonetheless implicitly cluster contacts, by virtue of their interactions with them, forming implicit groups. In this paper, we describe the implicit so ..."
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Cited by 51 (0 self)
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Although users of online communication tools rarely categorize their contacts into groups such as ”family”, ”co-workers”, or ”jogging buddies”, they nonetheless implicitly cluster contacts, by virtue of their interactions with them, forming implicit groups. In this paper, we describe the implicit social graph which is formed by users ’ interactions with contacts and groups of contacts, and which is distinct from explicit social graphs in which users explicitly add other individuals as their ”friends”. We introduce an interaction-based metric for estimating a user’s affinity to his contacts and groups. We then describe a novel friend suggestion algorithm that uses a user’s implicit social graph to generate a friend group, given a small seed set of contacts which the user has already labeled as friends. We show experimental results that demonstrate the importance of both implicit group relationships and interaction-based affinity ranking in suggesting friends. Finally, we discuss two applications of the Friend Suggest algorithm that have been released as Gmail Labs features.
mTrust: Discerning Multi-Faceted Trust in a Connected World
"... Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary ..."
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Cited by 29 (16 self)
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Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
Time-Based Sampling of Social Network Activity Graphs
"... While most research in online social networks (OSNs) in the past has focused on static friendship networks, social network activity graphs are quite important as well. However, characterizing social network activity graphs is computationally intensive; reducing the size of these graphs using samplin ..."
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Cited by 17 (5 self)
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While most research in online social networks (OSNs) in the past has focused on static friendship networks, social network activity graphs are quite important as well. However, characterizing social network activity graphs is computationally intensive; reducing the size of these graphs using sampling algorithms is critical. There are two important requirements—the sampling algorithm must be able to preserve core graph characteristics and be amenable to a streaming implementation since activity graphs are naturally evolving in a streaming fashion. Existing approaches satisfy either one or the other requirement, but not both. In this paper, we propose a novel sampling algorithm called Streaming Time Node Sampling (STNS) that exploits temporal clustering often found in real social networks. Using real communication data collected from Facebook and Twitter, we show that STNS significantly out-performs stateof-the-art sampling mechanisms such as node sampling and Forest Fire sampling, across both averages and distributions of several graph properties.
Prometheus: User-controlled p2p social data management for socially-aware applications
- 11th International Middleware Conference
, 2010
"... Abstract. Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information ..."
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Cited by 13 (9 self)
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Abstract. Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturallyformed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.
Transforming Graph Data for Statistical Relational Learning
, 2012
"... Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In th ..."
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Cited by 10 (4 self)
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Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.
Inferring Tie Strength from Online Directed Behavior
, 2012
"... Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (spec ..."
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Cited by 8 (1 self)
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Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (specifically, Facebook interactions) to successfully identify real-world strong ties. Ground truth was established by asking users themselves to name their closest friends in real life. We found the frequency of online interaction was diagnostic of strong ties, and interaction frequency was much more useful diagnostically than were attributes of the user or the user’s friends. More private communications (messages) were not necessarily more informative than public communications (comments, wall posts, and other interactions).
Measuring Tie Strength in Implicit Social Networks ⇤
"... Given a set of people and a set of events attended by them, we address the problem of measuring connectedness or tie strength between each pair of persons. The underlying assumption is that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to ..."
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Cited by 8 (1 self)
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Given a set of people and a set of events attended by them, we address the problem of measuring connectedness or tie strength between each pair of persons. The underlying assumption is that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms, which a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms. We then show that there is a range of tie-strength measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges of the social network (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. To settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order. This decision is best left to particular applications. We also classify existing tie-strength measures according to the axioms that they satisfy; and observe that none of the “self-referential ” tie-strength measures satisfy the axioms. In our experiments, we demonstrate the efficacy of our approach; show the completeness and soundness of our axioms, and present Kendall Tau Rank Correlation between various tie-strength measures. Author Keywords Social networks, tie strength, axiomatic approach
Applying Data Mining Techniques to Address Disaster Information Management Challenges on Mobile Devices
"... The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade. Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recove ..."
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Cited by 6 (4 self)
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The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade. Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recovery efforts after disaster event. With the proliferation of smart phones and wireless tablets, professionals who have an operational responsibility in disaster situations are relying on such devices to maintain communication. Further, with the rise of social media, technology savvy consumers are also using these devices extensively for situational updates. In this paper, we address several critical tasks which can facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We design
Detection of stealthy malware activities with traffic causality and scalable triggering relation discovery.
- In ASIACCS’14,
, 2014
"... ABSTRACT Studies show that a significant portion of networked computers are infected with stealthy malware. Infection allows remote attackers to control, utilize, or spy on victim machines. Conventional signature-scan or counting-based techniques are limited, as they are unable to stop new zero-day ..."
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Cited by 6 (5 self)
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ABSTRACT Studies show that a significant portion of networked computers are infected with stealthy malware. Infection allows remote attackers to control, utilize, or spy on victim machines. Conventional signature-scan or counting-based techniques are limited, as they are unable to stop new zero-day exploits. We describe a traffic analysis method that can effectively detect malware activities on a host. Our new approach efficiently discovers the underlying triggering relations of a massive amount of network events. We use these triggering relations to reason the occurrences of network events and to pinpoint stealthy malware activities. We define a new problem of triggering relation discovery of network events. Our solution is based on domain-knowledge guided advanced learning algorithms. Our extensive experimental evaluation involving 6+ GB traffic of various types shows promising results on the accuracy of our triggering relation discovery.
