arXiv subjects

Yishuo Shi

Publications and source records attributed to Yishuo Shi.

5 recordsLinked to original sources

arXiv2021

Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization with Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the state-of-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves up to 6.7 times of influence spread in large datasets over the state-of-the-art approaches.

cs.SI↗

arXiv2019

Seed Selection and Social Coupon Allocation for Redemption Maximization in Online Social Networks

Online social networks have become the medium for efficient viral marketing exploiting social influence in information diffusion. However, the emerging application Social Coupon (SC) incorporating social referral into coupons cannot be efficiently solved by previous researches which do not take into account the effect of SC allocation. The number of allocated SCs restricts the number of influenced friends for each user. In the paper, we investigate not only the seed selection problem but also the effect of SC allocation for optimizing the redemption rate which represents the efficiency of SC allocation. Accordingly, we formulate a problem named Seed Selection and SC allocation for Redemption Maximization (S3CRM) and prove the hardness of S3CRM. We design an effective algorithm with a performance guarantee, called Seed Selection and Social Coupon allocation algorithm. For S3CRM, we introduce the notion of marginal redemption to evaluate the efficiency of investment in seeds and SCs. Moreover, for a balanced investment, we develop a new graph structure called guaranteed path, to explore the opportunity to optimize the redemption rate. Finally, we perform a comprehensive evaluation on our proposed algorithm with various baselines. The results validate our ideas and show the effectiveness of the proposed algorithm over baselines.

cs.SI↗

arXiv2019

Approximation Algorithm for Minimum Weight $(k,m)$-CDS Problem in Unit Disk Graph

In a wireless sensor network, the virtual backbone plays an important role. Due to accidental damage or energy depletion, it is desirable that the virtual backbone is fault-tolerant. A fault-tolerant virtual backbone can be modeled as a $k$-connected $m$-fold dominating set ($(k,m)$-CDS for short). In this paper, we present a constant approximation algorithm for the minimum weight $(k,m)$-CDS problem in unit disk graphs under the assumption that $k$ and $m$ are two fixed constants with $m\geq k$. Prior to this work, constant approximation algorithms are known for $k=1$ with weight and $2\leq k\leq 3$ without weight. Our result is the first constant approximation algorithm for the $(k,m)$-CDS problem with general $k,m$ and with weight. The performance ratio is $(α+2.5kρ)$ for $k\geq 3$ and $(α+2.5ρ)$ for $k=2$, where $α$ is the performance ratio for the minimum weight $m$-fold dominating set problem and $ρ$ is the performance ratio for the subset $k$-connected subgraph problem (both problems are known to have constant performance ratios.)

cs.DM↗

arXiv2018

Approximation Algorithm for the Partial Set Multi-Cover Problem

Partial set cover problem and set multi-cover problem are two generalizations of set cover problem. In this paper, we consider the partial set multi-cover problem which is a combination of them: given an element set $E$, a collection of sets $\mathcal S\subseteq 2^E$, a total covering ratio $q$ which is a constant between 0 and 1, each set $S\in\mathcal S$ is associated with a cost $c_S$, each element $e\in E$ is associated with a covering requirement $r_e$, the goal is to find a minimum cost sub-collection $\mathcal S'\subseteq\mathcal S$ to fully cover at least $q|E|$ elements, where element $e$ is fully covered if it belongs to at least $r_e$ sets of $\mathcal S'$. Denote by $r_{\max}=\max\{r_e\colon e\in E\}$ the maximum covering requirement. We present an $(O(\frac{r_{\max}\log^2n}{\varepsilon}),1-\varepsilon)$-bicriteria approximation algorithm, that is, the output of our algorithm has cost at most $O(\frac{r_{\max}\log^2 n}{\varepsilon})$ times of the optimal value while the number of fully covered elements is at least $(1-\varepsilon)q|E|$.

cs.DM↗

arXiv2017

Randomized Bicriteria Approximation Algorithm for Minimum Submodular Cost Partial Multi-Cover Problem

This paper studies randomized approximation algorithm for a variant of the set cover problem called minimum submodular cost partial multi-cover (SCPMC), in which each element $e$ has a covering requirement $r_e$ and a profit $p_e$, and the cost function on sub-collection of sets is submodular, the goal is to find a minimum cost sub-collection of sets which fully covers at least $q$-percentage of total profit, where an element $e$ is fully covered by sub-collection $S'$ if and only if it belongs to at least $r_e$ sets of $\mathcal S'$. Previous work shows that such a combination enormously increases the difficulty of studies, even when the cost function is linear. In this paper, assuming that the maximum covering requirement $r_{\max}=\max_e r_e$ is a constant and the cost function is nonnegative, monotone nondecreasing, and submodular, we give the first randomized bicriteria algorithm for SCPMC the output of which fully covers at least $(q-\varepsilon)$-percentage of all elements and the performance ratio is $O(b/\varepsilon)$ with a high probability, where $b=\max_e\binom{f}{r_{e}}$ and $f$ is the maximum number of sets containing a common element. The algorithm is based on a novel non-linear program. Furthermore, in the case when the covering requirement $r\equiv 1$, a bicriteria $O(f/\varepsilon)$-approximation can be achieved even when monotonicity requirement is dropped off from the cost function.

cs.DS↗