[서평]「Component Software」 - Beyond Object-Oriented Programming -

We study the problem of online team formation. We consider a setting in which people possess different skills and compatibility among potential team members is modeled by a social network. A sequence of tasks arrives in an online fashion, and each task requires a specific set of skills. The goal is to form a new team upon arrival of each task, so that (i) each team possesses all skills required by the task, (ii) each team has small communication overhead, and (iii) the workload of performing the tasks is balanced among people in the fairest possible way.We propose efficient algorithms that address all these requirements: our algorithms form teams that always satisfy the required skills, provide approximation guarantees with respect to team communication overhead, and they are online-competitive with respect to load balancing. Experiments performed on collaboration networks among film actors and scientists, confirm that our algorithms are successful at balancing these conflicting requirements.This is the first paper that simultaneously addresses all these aspects. Previous work has either focused on minimizing coordination for a single task or balancing the workload neglecting coordination costs.

/pdf/online-team-formation-in-social-networks-5cjbpht99b.pdf

Online team formation in social networks

Given a graph G and a vertex q ∈ G, the community search query returns a subgraph of G that contains vertices related to q. Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and setting up of social events. In this paper, we investigate the attributed community query (or ACQ), which returns an attributed community (AC) for an attributed graph. The AC is a subgraph of G, which satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). The AC enables a better understanding of how and why a community is formed (e.g., members of an AC have a common interest in music, because they all have the same keyword "music"). An AC can be "personalized"; for example, an ACQ user may specify that an AC returned should be related to some specific keywords like "research" and "sports".To enable efficient AC search, we develop the CL-tree index structure and three algorithms based on it. We evaluate our solutions on four large graphs, namely Flickr, DBLP, Tencent, and DBpedia. Our results show that ACs are more effective and efficient than existing community retrieval approaches. Moreover, an AC contains more precise and personalized information than that of existing community search and detection methods.

/pdf/effective-community-search-for-large-attributed-graphs-2zwcoxg8j1.pdf

Effective community search for large attributed graphs

Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.

Graph Pattern Matching.

The worst case fall time is given by tf = 2.2 (2Rn•Cout + Rn•Cx) = 2.2(2*1 KΩ*8fF + 1KΩ*2fF) = 39.6ps b) The best-case rise time for a NAND gate occurs when both pMOS turn on and is given by tr = 2.2 (Rp/2) •Cout, where the total parallel resistance is Rp/2. = 2.2 * 2 KΩ/2 * 8 fF = 17.6 ps c) The worst-case rise time of a NOR gate is given by tr = 2.2 (2Rp•Cout + Rp•Cx) = 2.2(2*2 KΩ*8fF + 2KΩ*2fF) = 79.2 ps

Problem (2)

We study the problem of discovering a team of experts from a social network. Given a project whose completion requires a set of skills, our goal is to find a set of experts that together have all of the required skills and also have the minimal communication cost among them. We propose two communication cost functions designed for two types of communication structures. We show that the problem of finding the team of experts that minimizes one of the proposed cost functions is NP-hard. Thus, an approximation algorithm with an approximation ratio of two is designed. We introduce the problem of finding a team of experts with a leader. The leader is responsible for monitoring and coordinating the project, and thus a different communication cost function is used in this problem. To solve this problem, an exact polynomial algorithm is proposed. We show that the total number of teams may be exponential with respect to the number of required skills. Thus, two procedures that produce top-k teams of experts with or without a leader in polynomial delay are proposed. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed methods.

/pdf/discovering-top-k-teams-of-experts-with-without-a-leader-in-48qxbnqbia.pdf

Discovering top-k teams of experts with/without a leader in social networks

Keyword search over a graph finds a substructure of the graph containing all or some of the input keywords. Most of previous methods in this area find connected minimal trees that cover all the query keywords. Recently, it has been shown that finding subgraphs rather than trees can be more useful and informative for the users. However, the current tree or graph based methods may produce answers in which some content nodes (i.e., nodes that contain input keywords) are not very close to each other. In addition, when searching for answers, these methods may explore the whole graph rather than only the content nodes. This may lead to poor performance in execution time. To address the above problems, we propose the problem of finding r-cliques in graphs. An r-clique is a group of content nodes that cover all the input keywords and the distance between each two nodes is less than or equal to r. An exact algorithm is proposed that finds all r-cliques in the input graph. In addition, an approximation algorithm that produces r-cliques with 2-approximation in polynomial delay is proposed. Extensive performance studies using two large real data sets confirm the efficiency and accuracy of finding r-cliques in graphs.

http://www.cse.yorku.ca/~aan/research/paper/paper283_CameraReady.pdf

Keyword search in graphs: finding r-cliques

We tackle the problem of finding a team of experts from a social network to complete a project that requires a set of skills. The social network is modeled as a graph. A node in the graph represents an expert and has a weight representing the monetary cost for using the expert service. Two nodes in the graph can be connected and the weight on the edge represents the communication cost between the two corresponding experts. Given a project, our objective is to find a team of experts that covers all the required skills and also minimizes the communication cost as well as the personnel cost of the project. To minimize both of the objectives, we define a new combined cost function which is based on the linear combination of the objectives (i.e. communication and personnel costs). We show that the problem of minimizing the combined cost function is an NP-hard problem. Thus, one approximation algorithm is proposed to solve the problem. The proposed approximation algorithm is bounded and the approximation ratio of the algorithm is proved in the paper. Three heuristic algorithms based on different intuitions are also proposed for solving the problem. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.

/pdf/efficient-bi-objective-team-formation-in-social-networks-40g6gd17q8.pdf

Efficient bi-objective team formation in social networks

Given an expert network, we tackle the problem of finding a team of experts that covers a set of required skills and also minimizes the communication cost as well as the personnel cost of the team. Since two costs need to be minimized, this is a bicriteria optimization problem. We show that the problem of minimizing these objectives is NP-hard. We use two approaches to solve this bicriteria optimization problem. In the first approach, we propose several (α, β)-approximation algorithms that receive a budget on one objective and minimizes the other objective within the budget with guaranteed performance bounds. In the second approach, an approximation algorithm is proposed to find a set of Pareto-optimal teams, in which each team is not dominated by other feasible teams in terms of the personnel and communication costs. The proposed approximation algorithms have provable performance bounds. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.

/pdf/finding-affordable-and-collaborative-teams-from-a-network-of-310w6lw4x2.pdf

Finding Affordable and Collaborative Teams from a Network of Experts.

Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities.

Mehdi Kargar

Papers

Discovering top-k teams of experts with/without a leader in social networks

Keyword search in graphs: finding r-cliques

Efficient bi-objective team formation in social networks

Finding Affordable and Collaborative Teams from a Network of Experts.

User community detection via embedding of social network structure and temporal content